We apply the method of moments to the relativistic Boltzmann-Vlasov equation and derive the equations of motion for the irreducible moments of arbitrary tensor-rank of the invariant single-particle distribution function. We study two cases, in the first of which the moments are taken to be irreducible with respect to the little group associated with the time-like fluid four-velocity, while in the second case they are assumed to be also irreducible with respect to a space-like four-vector orthogonal to the fluid four-velocity, which breaks the spatial isotropy to a rotational symmetry in the plane transverse to this vector. A systematic truncation and closure of the general moment equations leads, in the first case, to a theory of relativistic higher-order dissipative resistive magnetohydrodynamics. In the second case, we obtain a novel theory of dissipative resistive anisotropic magnetohydrodynamics, where the momentum anisotropy is in principle independent from that introduced by the external magnetic field.

The accuracy of neutronics simulations of actual or future reactor cores is nowadays driven by the precision of the nuclear data used as input. Among the most important neutron-induced fission cross sections to understand well are the actinides. It is, indeed, of primary importance to know accurately these cross sections around 1 MeV for the safety of Generation IV reactors. High accuracy measurements of neutron flux are essential for accurate cross section measurements; measurements of this flux with respect to the 1 H(n,n)p cross section can be made with the proton recoil technique. For an accurate measurement below 1 MeV, the Gaseous Proton Recoil Telescope (GPRT) is developed and characterized, with the aim to provide quasi-absolute neutron flux measurements with an accuracy better than 2%. This detector is composed of a double ionization chamber with a Micromegas segmented detection plane. The pressure of the gas can be adjusted to protons stopping range -and therefore to neutrons energy. An accurate neutron flux measurement requires that the GPRT has an intrinsic efficiency of 100%, and thus an important effort has been made to verify this. An alpha source and proton micro-beam have been used and the intrinsic efficiency is confirmed to be 100%. Additionally, the dead-time of the detector has been investigated on a test bench, and is found to be 7.3 ms.

Recently, a p-GaN gate double channel HEMT (DC-HEMT) with conductivity modulation has been reported. The conductivity modulation is realized by hole storage in the gate stack and observed under quasi-static measurements. In this work, pulsed measurement and transient simulations of the DC-HEMT are carried out to disclose the conductivity modulation at high frequency and build-up time of hole storage. It takes 150 ns for the hole storage to be completely established despite a Schottky gate in the DC-HEMT with low gate leakage. The fast build-up of hole storage is attributed to the AlN insertion layer's strong confinement capability of holes and suppressed electron-hole recombination in the DC structure.

Laser wakefield accelerator experiments have made enormous progress over the past $\sim 20$ years, but their promise to revolutionize high-energy particle sources is only beginning to be realized. To make the next step toward engineering LWFAs for different accelerator outcomes, we need more reliable and quantitative models to predict performance. Using the data from $>50$ published experiments, we estimate scalings and the performance envelope. We compare the observed scalings with several models in the literature. We find that the total beam energy (centroid energy times beam charge) scales almost linearly with laser energy, supporting the value of investment in progressively higher energy driver lasers. The dataset includes pulse durations from 8 to 160 fs, but only laser wavelengths of 800 nm and 1 \si{\micro\meter}, meaning we could not check proposed wavelength scalings for alternative laser technologies. As a benchmark next-generation case, the observed scalings suggest that achieving a 100-GeV LWFA stage will require a $\gtrsim 30$ PW laser operating at electron density $<10^{17}/$cm$^3$.

A 200-member ensemble developed at the Center for Western Weather and Water Extremes based on the Weather Research and Forecast atmospheric model tailored for the prediction of atmospheric rivers and associated heavy-to-extreme precipitation events over the Western US (West-WRF) is presented. The ensemble (WW200En) is generated with initial and boundary conditions from the US National Center for Environmental Prediction's Global Ensemble Forecast System (GEFS) and the European Centre for Medium-Range Weather Forecasts' Ensemble Prediction System (EPS), 100 unique combinations of microphysics, planetary boundary layer, and cumulus schemes, as well as perturbations applied to each of the 200 members based on the stochastic kinetic-energy backscatter scheme. Each member is run with 9-km horizontal increments and 60 vertical levels for a 10-month period spanning two winters. The performance of WW200En is compared to GEFS and EPS for probabilistic forecasts of 24-h precipitation, integrated water vapor transport (IVT), and for several thresholds including high percentiles of the observed climatological distribution. The WW200En precipitation forecast skill is better than GEFS at nearly all thresholds and lead times, and comparable or better than the EPS. For larger rainfall thresholds WW200En typically exhibits the best forecast skill. Additionally, WW200En has a better spread-skill relationship than the global systems, and an improved overall reliability and resolution of the probabilistic prediction. The results for IVT are qualitatively similar to those for precipitation forecasts. A sensitivity analysis of the physics parameterizations and the number of ensemble members provides insights into possible future developments of WW200En.

The theory of plasmas, that is collectives of charged particles, is developed using the coordinate-free and geometric methods of exterior calculus. This dramatically simplifies the algebra and gives a geometric physical interpretation. The fundamental foundation on which the theory is built is the conservation of phase space volume expressed by the Generalized Liouville Equation in terms of the Lie derivative. The theory is expanded both in the order of the correlation and in the weakness of the correlation. This gives a Generalized BBGKY (Bogoliubov-Born-Green-Kirkwood-Yvon) Hierarchy. The derivation continues to give a new generalized formula for the Variational Theory of Reaction Rates (VTRR). Pullbacks of the generalized formulas to generic canonical coordinates and Poisson brackets are done. Where appropriate, the canonical coordinates are assumed to be "action-angle" coordinates that are generated by the solution to the Hamilton-Jacobi equation, the action. Finally, generalized forms of all the common kinetic equations are derived: the Vlasov Equation, the Boltzmann Equation, the Master Equation, the Fokker-Planck Equation, the Vlasov-Fokker-Planck (VFP) Equation, the Fluid Equations, and the MagnetoHydroDynamic (MHD) Equations. Specific examples are given of these equations. The application of the VTRR to three-body recombination in a strong magnetic field is shown.

$^{7}Be$ an isotope emanating from cosmogenic origins due to high energy cosmic rays, is studied from its production to its detection in the surface, in order to elucidate atmospheric circulation phenomena and analyze the vertical transport of air masses. This can be illustrated briefly by the monsoon model in Kerala in India, where the application of the $^{7}Be$ detection methods in stations in Russia and Australia offered predictions of the debut and retreat of moonsoon saison in contrast to the meteorological methods.

We propose a short-term wind forecasting framework for predicting real-time variations in atmospheric turbulence based on nacelle-mounted anemometer and ground-level air-pressure measurements. Our approach combines linear stochastic estimation and Kalman filtering algorithms to assimilate and process real-time field measurements with the predictions of a stochastic reduced-order model that is confined to a two-dimensional plane at the hub height of turbines. We bridge the vertical gap between the computational plane of the model at hub height and the measurement plane on the ground using a projection technique that allows us to infer the pressure in one plane from the other. Depending on the quality of this inference, we show that customized variants of the extended and ensemble Kalman filters can be tuned to balance estimation quality and computational speed 1-1.5 diameters ahead and behind leading turbines. In particular, we show how synchronizing the sign of estimates with that of velocity fluctuations recorded at the nacelle can significantly improve the ability to follow temporal variations upwind of the leading turbine. We also propose a convex optimization-based framework for selecting a subset of pressure sensors that achieve a desired level of accuracy relative to the optimal Kalman filter that uses all sensing capabilities.

Coherent control of atomic and molecular scattering relies on the preparation of colliding particles in superpositions of internal states, establishing interfering pathways that can be used to tune the outcome of a scattering process. However, incoherent addition of different partial wave contributions to the integral cross sections (partial wave scrambling), commonly encountered in systems with complex collisional dynamics, poses a significant challenge, often limiting control. This work demonstrates that time-reversal symmetry can overcome these limitations by constraining the relative phases of S-matrix elements, thereby protecting coherent control against partial wave scrambling, even for collisions mediated by highly anisotropic interactions. Using the example of ultracold O$_2$-O$_2$ scattering, we show that coherent control is robust against short-range dynamical complexity. Furthermore, the time-reversal symmetry also protects the control against a distribution of collisional energy. These findings show that ultracold scattering into the final states that are time-reversal-invariant, such as the J = 0, M = 0 rotational state, can always be optimally controlled by using time-reversal-invariant initial superpositions. Beyond the ultracold regime, we observe significant differences in the controllability of crossed-molecular beam vs. trap experiments with the former being easier to control, emphasizing the cooperative role of time-reversal and permutation symmetries in maintaining control at any temperature. These results open new avenues for the coherent control of complex inelastic collisions and chemical reactions both in and outside of the ultracold regime.

Ge-68 is a cosmogenic isotope in germanium with a half-life of 270.9 days. Ge-68 and its decay daughter Ga-68 contribute considerable background with energy up to 3 MeV to low background $\gamma$ spectrometers using high purity germanium (HPGe) detectors. In this paper, we evaluated the background of Ge-68 and Ga-68 in a p-type coaxial HPGe detector operated at China Jinping underground laboratory (CJPL) via a time series fitting method. Under the assumption that Ge-68 and Ga-68 are in radioactive equilibrium and airborne radon daughters are uniformly distributed in the measurement chamber of the spectrometer, we fit the time series of count rate in 1-3 MeV to calculate the Ge-68 activity, radon daughter concentrations, and the time-invariant background component. Total 90 days measured data were used in analysis, a hypothesis test confirmed a significant Ge-68 signal at 99.64% confidence level. The initial activity of Ge-68 is fitted to be 477.0$\pm$112.4 $\mu$Bq/kg, corresponding to an integral count rate of 55.9 count/day in 1-3 MeV range. During the measurement, Ge-68 activity decreased by about 30%, contributing about 62% of the total background in 1-3 MeV range. Our method also provides an estimation of the variation of airborne radon daughter concentrations in the measurement chamber, which could be used to monitor the performance of radon reduction measures.

The sensitive axes of atom gravimeters are defined by the directions of the respective Raman lasers. Any tilt of the Raman lasers with respect to the vertical direction introduces errors in gravity measurements. In this work, we report a fast determination of the tilt of Raman lasers, where the fringe of the atom interferometer is scanned by varying the tilt, rather than the phase, of the Raman lasers. Unlike the periodic cosine fringes typically used in atom interferometers, the fringe obtained by changing the tilt, referred to as the tilt-scanned fringe, is aperiodic and symmetric with respect to zero tilt. The tilt-scanned fringe is highly sensitive to asymmetries caused by non-zero tilt, enabling fast and precise determination of the Raman laser tilt in atom gravimeters. We demonstrate that one tilt-scanned fringe, corresponding to a measurement cycle time of 13 s, can determine the tilt with a typical precision of about 30 $\mu$rad in our developed atom gravimeter. Further investigation proves that the tilt-scanned fringe approach shortens the measurement cycle time by over an order of magnitude while keeping comparable precision with conventional tilt determination techniques. The fast tilt determination presented here is significant for the application of atom gravimeters, particularly in absolute gravity surveys.

This paper proposes a dual-color grating chip design method for simultaneously capturing dual atomic clouds (87Rb and 133Cs). By simulating key parameters such as the grating period, etching depth, duty cycle, coating material, and thickness, the optimal design parameters were determined to ensure efficient dual-wavelength diffraction and maximize the number of captured atoms. Experimental results demonstrate the simultaneous trapping of 1.6E8 87Rb atoms and 7.8E6 133Cs atoms, thereby offering an approach for multi-species cold atom systems. This dual-species grating magneto-optical trap (GMOT) system has potential applications in precision measurements such as cold atom clocks, quantum interferometers, and quantum electrometry.

Energetic or "hot" electrons and holes generated from the decay of localized surface plasmons in metallic nanoparticles have great potential for applications in photocatalysis, photovoltaics, and sensing. Here, we study the generation of hot carriers in brick-shaped gold nanoparticles using a recently developed modelling approach that combines a solution to Maxwell's equation with large-scale tight-binding simulations to evaluate Fermi's Golden Rule. We find that hot-carrier generation depends sensitively on the aspect ratio of the nanobricks with flatter bricks producing a large number of energetic electrons irrespective of the light polarization. In contrast, the hot-carrier generation rates of elongated nanobricks exhibits a strong dependence on the light polarization. The insights resulting from our calculations can be harnessed to design nanobricks that produce hot carriers with properties tailored to specific device applications.

Trichomes, specialized hair-like structures on the surfaces of many plants, play a crucial role in defense against herbivorous insects. We investigated the biomechanics of type VI glandular trichomes in cultivated tomato (Solanum lycopersicum) and its wild relative (Solanum habrochaites). Using micropipette force sensors and high-speed imaging, we uncovered the rupture mechanics underlying gland bursting, highlighting the small forces and short time-scales involved in this process. Additionally, we observed larvae of the Western flower thrips (Frankliniella occidentalis), a major pest in tomato cultivation, inadvertently triggering trichome rupture and accumulating glandular secretions on their bodies. These findings demonstrate how rapid gland bursting and the fluid dynamics of glandular secretions act as an efficient and swift plant defense mechanism against insect herbivory.

In this research, we investigate second-order sideband generation (SSG) and slow-fast light using a hybrid system comprised of two coupled opto- and magnomechanical microspheres, namely a YIG sphere and a silica sphere. The YIG sphere hosts a magnon mode and a vibration mode induced by magnetostriction, whereas the silica sphere has an optical whispering gallery mode and a mechanical mode coupled via optomechanical interaction. The mechanical modes of both spheres are close in frequency and are coherently coupled by the straightway physical contact between the two microspheres. We use a perturbation approach to solve the Heisenberg-Langevin equations, offering an analytical framework for transmission rate and SSG. Using experimentally feasible settings, we demonstrate that the transmission rate and SSG are strongly dependent on the magnomechanical, optomechanical, and mechanics mechanics coupling strengths (MMCS) between the two microspheres. The numerical results show that increasing the MMCS can enhance both the transmission rate and SSG efficiency, resulting in gain within our system. Our findings, in particular, reveal that the efficiency of the SSG can be effectively controlled by cavity detuning, decay rate, and pump power. Notably, our findings suggest that modifying the system parameters can alter the group delay, thereby regulating the transition between fast and slow light propagation, and vice versa. Our protocol provides guidelines for manipulating nonlinear optical properties and controlling light propagation, with applications including optical switching, information storage, and precise measurement of weak signals.

Shock tubes have become indispensable tools for advancing research across diverse fields such as aerodynamics, astrophysics, chemical kinetics, and industrial applications. The present study examines shock velocity variations in double-diaphragm shock tubes and their impact on flow dynamics at the end of the shock tube. Experiments were conducted using helium as the driver gas at 30 bar and argon as the driven gas at 100 Torr, with two different mid-section pressures (4.8 and 18.8 bar). A previous study highlighted two mechanisms governing the shock velocity profile: a higher peak shock velocity in the case of lower mid-section pressure due to stronger shock heating of the mid-section gas, and a second stage of acceleration of the shock wave resulting from the higher mid-section pressure and delayed opening of the first diaphragm. Pressure histories of the mid-section between the two diaphragms and high-speed imaging of the diaphragm-opening process helped validate the mechanisms influencing shock velocity. Additionally, numerical simulations were performed (i) to understand the effect of diaphragm-opening dynamics on the experimental shock profiles and (ii) to quantify the axial temperature as well as pressure profiles within the shocked gas. The simulations revealed that while axial pressure variations were not significantly affected by shock velocity variation, there were large temperature gradients in the shocked gas, with up to 6.5 percent higher peak temperature in the case of lower mid-section pressure compared to its counterpart. These pivotal findings underscore the need to control the mid-section pressure to minimize temperature variations within the shocked gas region.

One of the goals of personalized medicine is to tailor diagnostics to individual patients. Diagnostics are performed in practice by measuring quantities, called biomarkers, that indicate the existence and progress of a disease. In common cardiovascular diseases, such as hypertension, biomarkers that are closely related to the clinical representation of a patient can be predicted using computational models. Personalizing computational models translates to considering patient-specific flow conditions, for example, the compliance of blood vessels that cannot be a priori known and quantities such as the patient geometry that can be measured using imaging. Therefore, a patient is identified by a set of measurable and nonmeasurable parameters needed to well-define a computational model; else, the computational model is not personalized, meaning it is prone to large prediction errors. Therefore, to personalize a computational model, sufficient information needs to be extracted from the data. The current methods by which this is done are either inefficient, due to relying on slow-converging optimization methods, or hard to interpret, due to using `black box` deep-learning algorithms. We propose a personalized diagnostic procedure based on a differentiable 0D-1D Navier-Stokes reduced order model solver and fast parameter inference methods that take advantage of gradients through the solver. By providing a faster method for performing parameter inference and sensitivity analysis through differentiability while maintaining the interpretability of well-understood mathematical models and numerical methods, the best of both worlds is combined. The performance of the proposed solver is validated against a well-established process on different geometries, and different parameter inference processes are successfully performed.

The GEO satellite maintains good synchronization with the ground, reducing the priority of acquisition time in the establishment of the optical link. Whereas energy is an important resource for the satellite to execute space missions, the consumption during the acquisition process rises to the primary optimization objective. However, no previous studies have addressed this issue. Motivated by this gap, this paper first model the relationship between the transmitted power and the received SNR in the coherent detection system, with the corresponding single-field acquisition probability, the acquisition time is then calculated, and the closed-form expression of the multi-field acquisition energy consumption is further derived in scan-stare mode. Then for dual-scan technique, through the induction of the probability density function of acquisition energy, it is transformed into the equivalent form of scan-stare, thereby acquiring acquisition energy. Subsequently, optimizations are performed on these two modes. The above theoretical derivations are verified through Monte Carlo simulations. Consequently, the acquisition energy of dual-scan is lower than that of scan-stare, with the acquisition time being about half of the latter, making it a more efficient technique. Notably, the optimum beam divergence angle is the minimum that the laser can modulate, and the beaconless acquisition energy is only 6\% of that with the beacon, indicating that the beaconless is a better strategy for optical link acquisition with the goal of energy consumption optimization.

In the realm of lithography, Optical Proximity Correction (OPC) is a crucial resolution enhancement technique that optimizes the transmission function of photomasks on a pixel-based to effectively counter Optical Proximity Effects (OPE). However, conventional pixel-based OPC methods often generate patterns that pose manufacturing challenges, thereby leading to the increased cost in practical scenarios. This paper presents a novel inverse lithographic approach to OPC, employing a model-driven, block stacking deep learning framework that expedites the generation of masks conducive to manufacturing. This method is founded on vector lithography modelling and streamlines the training process by eliminating the requirement for extensive labeled datasets. Furthermore, diversity of mask patterns is enhanced by employing a wave function collapse algorithm, which facilitates the random generation of a multitude of target patterns, therefore significantly expanding the range of mask paradigm. Numerical experiments have substantiated the efficacy of the proposed end-to-end approach, highlighting its superior capability to manage mask complexity within the context of advanced OPC lithography. This advancement is anticipated to enhance the feasibility and economic viability of OPC technology within actual manufacturing environments.

This paper presents a comprehensive study of a compact two-element meandered dipole array aimed at achieving super-realized gain. An optimization was performed using a Genetic Algorithm (GA) implemented in MATLAB, targeting the maximization of the realized gain in the end-fire direction. Three primary excitation schemes were evaluated: the conventional optimization to achieve superdirectivity (signal magnitude and phase angle), phase-only optimization, and uniform excitation (characterized by equal amplitude and no phase shift). The results show that by carefully optimizing both the signal magnitudes and phase angles, the array could achieve a substantial improvement in realized gain. Phase-only optimization provided a competitive realized gain with only minor reductions compared to conventional optimization, suggesting that optimizing the signal phase alone can be an effective strategy in practical implementations.

Understanding alterations in structural disorders in tissue or cells or building blocks, such as DNA or chromatin in the human brain, at the nano to submicron level provides us with efficient biomarkers for Alzheimers detection. Here, we report a dual photonics technique to detect nano- to submicron-scale alterations in brain tissues or cells and DNA or chromatin due to the early to late progression of Alzheimers disease in humans. Using a recently developed mesoscopic light transport technique, fine-focused nano-sensitive partial wave spectroscopy (PWS), we measure the degree of structural disorder in tissues. Furthermore, the chemical-specific inverse participation ratio technique (IPR) was used to measure the DNA or chromatin structural alterations. The results of the PWS and IPR experiments showed a significant increase in the degree of structural disorder at the nano to submicron scale at different stages of AD relative to their controls for both the tissue or cell and DNA cellular levels. The increase in the structural disorder in cells or tissues and DNA or chromatin in the nuclei can be attributed to higher mass density fluctuations in the tissue and DNA or chromatin damage in the nuclei caused by the rearrangements of macromolecules due to the deposition of the amyloid beta protein and damage in DNA or chromatin with the progress of AD.

We propose a novel scheme for generating and accelerating simultaneously a dozen-GeV isolated attosecond electron bunch from an electron beam-driven hollow-channel plasma target. During the beam-target interaction, transverse oscillations of plasma electrons are induced, and subsequently, a radiative wakefield is generated. Meanwhile, a large number of plasma electrons of close to the speed of light are injected transversely from the position of the weaker radiative wakefield (e.g., the half-periodic node of the radiative wakefield) and converge towards the center of the hollow channel, forming an isolated attosecond electron bunch. Then, the attosecond electron bunch is significantly accelerated to high energies by the radiative wakefield. It is demonstrated theoretically and numerically that this scheme can efficiently generate an isolated attosecond electron bunch with a charge of more than 2 nC, a peak energy up to 13 GeV of more than 2 times that of the driving electron beam, a peak divergence angle of less than 5 mmrad, a duration of 276 as, and an energy conversion efficiency of 36.7% as well as a high stability as compared with the laser-beam drive case. Such an isolated attosecond electron bunch in the range of GeV would provide critical applications in ultrafast physics and high energy physics, etc.

This work shows an anomalously enhanced response of the low-latitude ionosphere over the Indian sector under weak geomagnetic conditions (October 31, 2021) in comparison to a stronger event (November 04, 2021) under the influence of an Interplanetary Coronal Mass Ejection (ICME)-driven Magnetic Cloud (MC)-like and sheath regions respectively. The investigation is based on measurements of the Total Electron Content (TEC) from Ahmedabad (23.06$^\circ$N, 72.54$^\circ$E, geographic; dip angle: 35.20$^\circ$), a location near the northern crest of the Equatorial Ionization Anomaly (EIA) over the Indian region. During the weaker event, the observed TEC from the Geostationary Earth Orbit (GEO) satellites of Navigation with Indian Constellation (NavIC), showed diurnal maximum enhancements of about 20 TECU over quiet-time variations, as compared to the stronger event where no such enhancements are present. It is shown that storm intensity (SYM-H) or magnitude of the southward Interplanetary Magnetic Field (IMF) alone is unable to determine the ionospheric impacts of this space weather event. However, it is the non-fluctuating southward IMF and the corresponding penetration electric fields, for a sufficient interval of time, in tandem with the poleward neutral wind variations, that determines the strengthening of low-latitude electrodynamics of this anomalous event of October 31, 2021. Therefore, the present investigation highlights a case for further investigations of the important roles played by non-fluctuating penetration electric fields in determining a higher response of the low-latitude ionosphere even if the geomagnetic storm intensities are significantly low.

Phase is an intrinsic property of light, and thus a crucial parameter across numerous applications in modern optics. Various methods exist for measuring the phase of light, each presenting challenges and limitations-from the mechanical stability requirements of free-space interferometers to the computational complexity usually associated with methods based on spatial light modulators. Here, we utilize a passive photonic integrated circuit to spatially probe phase and intensity distributions of free-space light beams. Phase information is encoded into intensity through a set of passive on-chip interferometers, allowing conventional detectors to retrieve the phase profile of light through single-shot intensity measurements. Furthermore, we use silicon nitride as material platform for the waveguide architecture, facilitating broadband utilization in the visible spectral range. Our approach for fast, broadband, and spatially resolved measurement of intensity and phase enables a wide variety of potential applications, ranging from microscopy to free-space optical communication.

We present a model for the particle balance in the post-disruption runaway electron plateau phase of a tokamak discharge. The model is constructed with the help of, and applied to, experimental data from TCV discharges investigating the so-called "low-Z benign termination" runaway electron mitigation scheme. In the benign termination scheme, the free electron density is first reduced in order for a subsequently induced MHD instability to grow rapidly and spread the runaway electrons widely across the wall. The model explains why there is an upper limit for the neutral pressure above which the termination is not benign. We are also able to show that the observed non-monotonic dependence of the free electron density with the measured neutral pressure is due to plasma re-ionization induced by runaway electron impact ionization. At higher neutral pressures, more target particles are present in the plasma for runaway electrons to collide with and ionize. Parameter scans are conducted to clarify the role of the runaway electron density and energy on the upper pressure limit, and it is found that only the runaway electron density has a noticeable impact.

The discovery of laser wakefield acceleration in gaseous plasma was a major milestone that could lead to a significant reduction of size and cost of large electron accelerators. For higher-energy laser-driven electron acceleration guiding plasma channels were proposed, which are matched to the laser pulse parameters. For guiding a Gaussian beam, a parabolic density profile is needed, which is difficult to realize experimentally. The realistic channel profiles can be described by higher order polynomial functions which are not optimal for guiding due to the development of undesired distortions in the laser intensity envelope. However, here we show that for non-parabolic plasma channels well-defined matching conditions exist, which we call mode matching. This leads to the guiding of the fundamental mode only in the acceleration regime, where the plasma electron density is modulated by the high-intensity laser pulse. In this way, we eliminate two deteriorating factors of laser wakefield acceleration, namely the mode dispersion and energy leakage. We apply this new matching condition for single-mode guiding in quasi-3D simulations to show that 10 GeV energies can be reached in a distance of less than 15 cm.

We demonstrate a technique for ultrastable optical frequency dissemination in a branching passive optical network using code-division multiple access (CDMA). In our protocol, each network user employs a unique pseudo-random sequence to rapidly change the optical frequency among many distinct frequencies. After transmission through the optical network, each user correlates the received sequence with the transmitted one, thus establishing a frequency-hopping spread spectrum technique that helps reject optical signals transmitted by other users in the network. Our method, which builds on the work by Schediwy et al. [Opt. Lett. {\bf 38}, 2893 (2013)], improves the frequency distribution network's capacity, helps reject phase noise caused by intermediate optical back scattering, and simplifies the operational requirements. Using this protocol, we show that a frequency instability better than ~$10^{-18}$ while having more than 100 users operating in the network should be possible. Finally, we theoretically explore the limits of this protocol and show that the demonstrated stability does not suffer from any fundamental limitation. In the future, the CDMA method presented here could be used in complex time-frequency distribution networks, allowing more users while, at the same time, reducing the network's complexity.

Neutral resonant states of molecules play a very important role in the dissociation dynamics and other electronic processes that occur via intermediate capture into these states. With the goal of identifying resonant states, and their corresponding widths, of the imidogen molecule NH as a function of internuclear distance, we have performed detailed R-matrix calculations on the e + NH+ system. In a previous work, we had identified bound states of NH and Feshbach resonances in the e + NH+ system at a single geometry, namely the NH+ equilibrium Re = 2.0205 a0 . Here we present a much more detailed work by repeating the calculation on over 60 internuclear distances to obtain the corresponding potential energy curves. The bound states for nine symmetries have been detailed many of which, particularly the singlet states, were never studied before. Several resonant states of different symmetries, which were unknown until now, have been systematically identified and their widths calculated in the present work, which proved much more challenging due to presence of many avoided crossings. It is hoped that the bound and the new resonant states obtained here will open up other molecular dynamics studies, since for several dissociative processes, although experimental data existed for more than a decade, these are still uncorroborated due to absence of molecular data, and hence subsequent theoretical calculations.

We present a method to achieve reaction-limited evaporation for the color-gradient lattice Boltzmann multicomponent model. Our approach involves a systematic way to remove fluid mass from the interface region in order to achieve evaporation rates similar to those in a reaction-limited regime. Through various tests, our method demonstrates accurate and consistent results for different interface shapes across a wide range of evaporation flux magnitudes. A single free parameter is required to choose the evaporation sites where fluid mass is exchanged between the components. We find that at unit density ratio, this single parameter allows for the correct description of an arbitrarily shaped interface with an error of less than 5%. For density contrasts, accurate results are observed for lower evaporation flux magnitudes and density ratios. Our proposed method can be applied to isothermal reaction-limited scenarios, such as evaporation in pure vapor or under a gas draft. It can also handle weakly space-time-dependent fluxes, making it suitable for specific non-isothermal applications such as drop evaporation from heated substrates.

Rydberg atoms are used in a wide range of applications due to their peculiar properties like strong dipolar and van der Waals interactions. The choice of Rydberg state has a huge impact on the strength and angular dependence of the interactions, and so a detailed understanding of the underlying processes and resulting properties of the interactions is therefore key to select the most suitable states for experiments. We study the van der Waals interactions in alkali atoms in detail and highlight the structures which allow an understanding and exploitation of the various interaction properties. A particular theme is the identification of F\"orster resonances with $n_1 \neq n_2$, which offer interaction potentials with a wide range of properties that make them particularly interesting for experimental applications. A second theme is a focus on the underlying structures that shape the angular dependency and sign of the interactions. This understanding -- instead of brute-force calculations -- allows for a much simpler and more systematic search for suitable pair states. These insights can be used for the selection of tailored interaction potentials subject to experimental constraints and requirements. We use rubidium as an example species in this work and also provide data for cesium and pair states that are coupled via two- or three-photon transitions, i.e. up to F states, in the appendix.

Networks model the architecture backbone of complex systems. The backbone itself can change over time leading to what is called `temporal networks'. Interpreting temporal networks as trajectories in graph space of a latent graph dynamics has recently enabled the extension of concepts and tools from dynamical systems and time series to networks. Here we address temporal networks with unlabelled nodes, a case that has received relatively little attention so far. Situations in which node labelling cannot be tracked over time often emerge in practice due to technical challenges, or privacy constraints. In unlabelled temporal networks there is no one-to-one matching between a network snapshot and its adjacency matrix. Characterizing the dynamical properties of such unlabelled network trajectories is nontrivial. We here exploit graph invariants to extend to the unlabelled setting network-dynamical quantifiers of linear correlations and dynamical instability. In particular, we focus on autocorrelation functions and the sensitive dependence on initial conditions. We show with synthetic graph dynamics that the measures are capable of recovering and estimating these dynamical fingerprints even when node labels are unavailable. We also validate the methods for some empirical temporal networks with removed node labels.

Piecewise omnigenous fields are stellarator magnetic fields that are optimized with respect to radial neoclassical transport thanks to a second adiabatic invariant that is piecewisely constant on the flux-surface. They are qualitatively different from omnigenous fields (including quasi-isodynamic or quasisymmetric fields), for which the second adiabatic invariant is a flux-surface constant. Piecewise omnigenous fields thus open an alternative path towards stellarator reactors. In this work, piecewise omnigenous fields are characterized and parametrized in a systematic manner. This is a step towards including piecewise omnigenity as an explicit design criterion in stellarator optimization, and towards a systematic study of the properties of nearly piecewise omnigenous stellarator configurations.

The history of the discovery of nuclear fission and how science fiction had anticipated it.

Two-dimensional optical spectroscopy experiments have shown that exciton transfer pathways in the Fenna-Matthews-Olson (FMO) photosynthetic complex differ drastically under reduced and oxidised conditions, suggesting a functional role for collective vibronic mechanisms that may be active in the reduced form but attenuated in the oxidised state. Higgins et al. [PNAS 118 (11) e2018240118 (2021)] used Redfield theory to link the experimental observations to altered exciton transfer rates due to oxidative onsite energy shifts that detune excitonic energy gaps from a specific vibrational frequency of the bacteriochlorophyll (BChl) a. Using a memory kernel formulation of the hierarchical equations of motion, we present non-perturbative estimations of transfer rates that yield a modified physical picture. Our findings indicate that onsite energy shifts alone cannot reproduce the observed rate changes in oxidative environments, either qualitatively or quantitatively. By systematically examining combined changes both in site energies and the local environment for the oxidised complex, while maintaining consistency with absorption spectra, our results suggest that vibronic tuning of transfer rates may indeed be active in the reduced complex. However, we achieve qualitative, but not quantitative, agreement with the experimentally measured rates. Our analysis indicates potential limitations of the FMO electronic Hamiltonian, which was originally derived by fitting spectra to second-order cumulant and Redfield theories. This suggests that reassessment of these electronic parameters with a non-perturbative scheme, or derived from first principles, is essential for a consistent and accurate understanding of exciton dynamics in FMO under varying redox conditions.

We present a new approach to investigating Rydberg molecules by demonstrating the formation and characterization of individual Rb$^{*}$Cs Rydberg molecules using optical tweezers. By employing single-atom detection of Rb and Cs, we observe molecule formation via correlated loss of both species and study the formation dynamics with single-particle resolution. We control the interatomic distances by manipulating the relative wavefunction of atom pairs using the tweezer intensity, optimizing the coupling to molecular states and exploring the effect of the tweezer on these states. Additionally, we demonstrate molecule association with atoms trapped in separate tweezers, paving the way for state-selective assembly of polyatomic molecules. The observed binding energies, molecular alignment, and bond lengths are in good agreement with theory. Our approach is broadly applicable to Rydberg tweezer platforms, expanding the range of available molecular systems and enabling the integration of Rydberg molecules into existing quantum science platforms.

Turbines are crucial to our energy infrastructure, and ensuring their bearings function with minimal friction while often supporting heavy loads is vital. Vibrations within a bearing can signal the presence of defects, friction, or misalignment. However, current detection methods are neither robust nor easy to automate. We propose a more quantitative approach by modelling the elastic waves within bearing raceways. By approximating the raceway as a hollow cylinder, we derive straightforward 4x4 systems for its vibrational modes, enabling both forward and inverse problem-solving. We also demonstrate how to significantly reduce the number of required sensors by using a simple prior: the known number of rollers and their angular speed. We present numerical examples showcasing the full recovery of contact traction between bearings and the raceway, as well as the detection of elastic emissions.

Building on our recent study [https://doi.org/10.1021/acs.jpclett.3c02052, J. Phys. Chem. Lett. 14, 8780 (2023)], we explore the generalization of the ground-state Kohn-Sham (KS) formalism of density-functional theory (DFT) to the (singlet) excited states of the asymmetric Hubbard dimer at half-filling. While we found that the KS-DFT framework can be straightforwardly generalized to the highest-lying doubly-excited state, the treatment of the first excited state presents significant challenges. Specifically, using a density-fixed adiabatic connection, we show that the density of the first excited state lacks non-interacting $v$-representability. However, by employing an analytic continuation of the adiabatic path, we demonstrate that the density of the first excited state can be generated by a complex-valued external potential in the non-interacting case. More practically, by performing state-specific KS calculations with exact and approximate correlation functionals -- each state possessing a distinct correlation functional -- we observe that spurious stationary solutions of the KS equations may arise due to the approximate nature of the functional.

In this paper we present a new thin-wall eddy current modeling code, ThinCurr, for studying inductively-coupled currents in 3D conducting structures -- with primary application focused on the interaction between currents flowing in coils, plasma, and conducting structures of magnetically-confined plasma devices. The code utilizes a boundary finite element method on an unstructured, triangular grid to accurately capture device structures. The new code, part of the broader Open FUSION Toolkit, is open-source and designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. Scalability to large models is enabled through use of hierarchical off-diagonal low-rank compression of the inductance matrix, which is otherwise dense. Ease of handling large models of complicated geometry is further supported by automatic determination of supplemental elements through a greedy homology approach. A detailed description of the numerical methods of the code and verification of the implementation of those methods using cross-code comparisons against the VALEN code and Ansys commercial analysis software is shown.

Low-rank regularization is an effective technique for addressing ill-posed inverse problems when the unknown variable exhibits low-rank characteristics. However, global low-rank assumptions do not always hold for seismic wavefields; in many practical situations, local low-rank features are instead more commonly observed. To leverage this insight, we propose partitioning the unknown variable into tiles, each represented via low-rank factorization. We apply this framework to regularize multidimensional deconvolution in the frequency domain, considering two key factors. First, the unknown variable, referred to as the Green's function, must maintain symmetry according to the reciprocity principle of wave propagation. To ensure symmetry within the tile-based low-rank framework, diagonal tiles are formulated as the product of a low-rank factor and its transpose if numerically rank-deficient. Otherwise, they are represented by preconditioned dense forms. Symmetry in off-diagonal elements is achieved by parameterizing sub-diagonal tiles as the product of two distinct low-rank factors, with the corresponding super-diagonal tiles set as their transposes. Second, the rank of the Green's function varies with frequency; in other words, the Green's function has different ranks at different frequencies. To determine the numerical rank and optimal tile size for each frequency, we first solve the multidimensional deconvolution problem using a benchmark solver. Based on these results, we estimate the optimal tile size and numerical rank for our proposed solver.

We employ a multiscale computational approach to investigate the condensation process of the C-terminal low-complexity region of the Caprin1 protein as a function of increasing ATP concentration for three states: the initial mixed state, nanocondensate formation, and the dissolution of the droplet as it reenters the mixed state. We show that upon condensation ATP assembles via pi-pi interactions, resulting in the formation of a large cluster of stacked ATP molecules stabilized by sodium counterions. The surface of the ATP assembly interacts with the arginine-rich regions of the Caprin1 protein, particularly with its N-terminus, to promote the complete phase-separated droplet on a lengthscale of tens of nanometers. In order to understand droplet stability, we analyze the near-surface electrostatic potential (NS-ESP) of Caprin1 and estimate the zeta potential of the Caprin1-ATP assemblies. We predict a positive NS-ESP at the Caprin1 surface for low ATP concentrations that defines the early mixed state, in excellent agreement with the NS-ESP obtained from NMR experiments using paramagnetic resonance enhancement. By contrast, the NS-ESP of Caprin1 at the surface of the nanocondensate at moderate levels of ATP is highly negative compared to the mixed state, and estimates of a large zeta potential outside the highly dense region of charge further explains the remarkable stability of this phase separated droplet assembly. As ATP concentrations rise further, the strong electrostatic forces needed for nanocondensate stability are replaced by weaker Caprin1-ATP interactions that drive the reentry into the mixed state that exhibits a much lower zeta potential.

We investigate the polarization of the electron acquired during the inelastic resonant scattering on hydrogen-like ions initially being in the ground state. The formation and subsequent Auger decay of the intermediate (3l3l') doubly excited states in the resonant channel modify the mechanism of polarization change by enhancing both spin-orbit and exchange interactions. Consequently, in the presence of the resonant channel, the acquired polarization can be clearly observed even for light ions when it is challenging to discern which state of the ion was excited in the process. We also show that the energy dependence of the polarization parameter clearly demonstrates strong interference both between the contributions of specific autoionizing states in the resonant channel and between the non-resonant and resonant channels.

Whole-body PET imaging is often hindered by respiratory motion during acquisition, causing significant degradation in the quality of reconstructed activity images. An additional challenge in PET/CT imaging arises from the respiratory phase mismatch between CT-based attenuation correction and PET acquisition, leading to attenuation artifacts. To address these issues, we propose two new, purely data-driven methods for the joint estimation of activity, attenuation, and motion in respiratory self-gated TOF PET. These methods enable the reconstruction of a single activity image free from motion and attenuation artifacts. The proposed methods were evaluated using data from the anthropomorphic Wilhelm phantom acquired on a Siemens mCT PET/CT system, as well as 3 clinical FDG PET/CT datasets acquired on a GE DMI PET/CT system. Image quality was assessed visually to identify motion and attenuation artifacts. Lesion uptake values were quantitatively compared across reconstructions without motion modeling, with motion modeling but static attenuation correction, and with our proposed methods. For the Wilhelm phantom, the proposed methods delivered image quality closely matching the reference reconstruction from a static acquisition. The lesion-to-background contrast for a liver dome lesion improved from 2.0 (no motion correction) to 5.2 (proposed methods), matching the contrast from the static acquisition (5.2). In contrast, motion modeling with static attenuation correction yielded a lower contrast of 3.5. In patient datasets, the proposed methods successfully reduced motion artifacts in lung and liver lesions and mitigated attenuation artifacts, demonstrating superior lesion to background separation. Our proposed methods enable the reconstruction of a single, high-quality activity image that is motion-corrected and free from attenuation artifacts, without the need for external hardware.

We present the results from a numerical investigation using the finite element method to study the buckling strength of near-perfect spherical shells containing a single, localized, Gaussian-dimple defect whose profile is systematically varied toward the limit of vanishing amplitude. In this limit, our simulations reveal distinct buckling behaviors for hemispheres, full spheres, and partial spherical caps. Hemispherical shells exhibit boundary-dominated buckling modes, resulting in a knockdown factor of 0.8. By contrast, full spherical shells display localized buckling at their pole with knockdown factors near unity. Furthermore, for partial spherical shells, we observed a transition from boundary modes to these localized buckling modes as a function of the cap angle. We characterize these behaviors by systematically examining the effects of the discretization level, solver parameters, and radius-to-thickness ratio on knockdown factors. Specifically, we identify the conditions under which knockdown factors converge across shell configurations. Our findings highlight the critical importance of carefully controlled numerical parameters in shell-buckling simulations in the near-perfect limit, demonstrating how precise choices in discretization and solver parameters are essential for accurately predicting the distinct buckling modes across different shell geometries.

We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical Langevin diffusion and a piecewise deterministic Markov process, where the expensive computation of long-range pairwise interactions is replaced by a resampling of the velocities at random times. This framework allows for an acceleration in the simulation speed while preserving sampling and dynamical properties such as the diffusion constant. It can also be integrated in classical multi-timestep methods, pushing further the computational speedup, while avoiding some of the resonance issues of the latter thanks to the random nature of jumps. The JUMP, JUMP-RESPA and JUMP-RESPA1 integrators have been implemented in the GPU-accelerated version of the Tinker-HP package and are shown to provide significantly enhanced performances compared to their BAOAB, BAOAB-RESPA and BAOAB-RESPA1 counterparts respectively.

Pellets of frozen material travelling into a magnetically confined fusion plasma are accelerated by the so-called pellet rocket effect. The non-uniform plasma heats the pellet ablation cloud asymmetrically, producing pressure-driven, rocket-like propulsion of the pellet. We present a semi-analytical model of this process by perturbing a spherically symmetric ablation model. Predicted pellet accelerations match experimental estimates in current tokamaks ($\sim 10^5 \;\rm m/s^2$). Projections for ITER high-confinement scenarios ($\sim 10^6 \;\rm m/s^2$) indicate significantly shorter pellet penetration than expected without this effect, which could limit the effectiveness of disruption mitigation.

The rotational states of ultracold polar molecules possess long radiative lifetimes, microwave-domain coupling, and tunable dipolar interactions. Coherent dynamics between pairs of rotational states have been used to demonstrate simple models of quantum magnetism and to manipulate quantum information stored as qubits. The availability of numerous rotational states has led to many proposals to implement more complicated models of quantum magnetism, higher-dimensional qudits, and intricate state networks as synthetic dimensions; however, these are yet to be experimentally realised. The primary issue limiting their implementation is the detrimental effect of the optical trapping environment on coherence, which is not easily mitigated for systems beyond two levels. To address this challenge, we investigate the applicability of magic-wavelength optical tweezer traps to facilitate multitransition coherence between rotational states. We demonstrate simultaneous second-scale coherence between three rotational states. Utilising this extended coherence, we perform multiparameter estimation using a generalised Ramsey sequence and demonstrate coherent spin-1 dynamics encoded in the rotational states. Our work paves the way to implementing proposed quantum simulation, computation, and metrology schemes that exploit the rich rotational structure of ultracold polar molecules.

Superfluid helium, the inviscid low-temperature phase of liquid \4He, enables investigation of flows with reduced dimensionality since, due to the vanishing viscosity, sub-micron flow channels can be constructed. In such strongly confined volumes filled with superfluid, the longitudinal acoustic wave is a coupled fluctuation of pressure and entropy density called fourth sound. In this work, we use multiple 4th sound acoustic modes inside a nano-superfluidic acoustic resonator in a pump-probe arrangement to observe localized clusters of quantized vortices leading to two-dimensional turbulence. The localised turbulence enables controllable and asymmetric dissipative coupling between acoustic modes. Furthermore, we derive a general procedure for analytically estimating the superfluid acoustic resonance frequencies inside a volume with mechanically compliant walls. Our work confirms earlier assumptions that turbulence in similar nanofluidic systems initially develops in localized areas of high shear. The multimode pump-probe methods presented here will allow future experiments to study the dynamics of two-dimensional quantum turbulence, e.g., the free decay.

Purpose: To theoretically and experimentally study implant lead tip heating caused by radiofrequency (RF) power deposition in different wire configurations that contain loop(s). Methods: Maximum temperature rise caused by RF heating was measured at 1.5T on 20 insulated, capped wires with various loop and straight segment configurations. The experimental results were compared with predictions from the previously reported simple exponential and the adapted transmission line models, as well as with a long-wavelength approximation. Results: Both models effectively predicted the trends in lead tip temperature rise for all the wire configurations, with the adapted transmission line model showing superior accuracy. For superior/inferior (S/I)-oriented wires, increasing the number of loops decreased the overall heating. However, when wires were oriented right/left (R/L) where the x-component of the electric field is negligible, additional loops increased the overall heating. Conclusion: The simple exponential and the adapted transmission line models previously developed for, and tested on, straight wires require no additional terms or further modification to account for RF heating in a variety of loop configurations. These results extend the usefulness of the models to manage implanted device lead tip heating and provide theoretical insight regarding the role of loops and electrical lengths in managing RF safety of implanted devices.

Deformed droplets are ubiquitous in various industrial applications, such as inkjet printing, lab-on-a-chip devices, and spray cooling, and can fundamentally affect the involved applications both favorably and unfavorably. Here, we employ many-body dissipative particle dynamics to investigate the oscillations of water droplets on a harmonically and horizontally vibrating, solid substrate. Three distinct scenarios of oscillations as a response to the substrate vibrations have been identified. The first scenario reflects a common situation where the droplet can follow the substrate vibrations. In the other two scenarios, favored in the case of hydrophilic substrates, droplet oscillations generate high shear rates that ultimately lead to droplet breakup. Leveraging our simulation model, the properties of the droplet and the mechanisms related to the oscillations are analyzed with a molecular-level resolution, while results are also put in the perspective of experiment. Our study suggests that the three scenarios can be distinguished by the contact-surface velocity of the oscillating droplet, with threshold velocities influenced by the substrate's wettability. Moreover, the mean magnitude of the particle velocity at the contact surface plays a key role in determining the three oscillation phases, suggesting that the capillary number of the oscillating droplet governs the phase behavior. Thus, our approach aims to optimize droplet oscillations and deformations on solid substrates, which have direct implications for technological applications.

The integrated photonics CMOS-compatible silicon nitride (SiN) platform is praised for its low propagation loss, but is limited by its lack of active functionalities such as a strong Pockels coefficient and intrinsic \c{hi}(2) nonlinearity. In this paper, we demonstrate the integration of centimetre-long thin-film lithium niobate (TFLN) devices on a SiN platform using the micro-transfer printing (uTP) method. At a wavelength of 1550 nm, propagation losses of approximately 0.9 dB/cm and transition losses of 1.8 dB per facet were measured. Furthermore, the TFLN was integrated into an imbalanced push-pull Mach-Zehnder modulator, achieving a V{\pi} of 3.2 V. The electro-optics nature of the observed modulation is confirmed by measuring the device up to 35 GHz, showing that the printing does not affect the high-speed LN properties.

Spatial analysis can generate both exogenous and endogenous biases, which will lead to ethics issues. Exogenous biases arise from external factors or environments and are unrelated to internal operating mechanisms, while endogenous biases stem from internal processes or technologies. Although much attention has been given to exogenous biases, endogenous biases in spatial analysis have been largely overlooked, and a comprehensive methodology for addressing them is yet to be developed. To tackle this challenge, we propose that visual analytics can play a key role in understanding geographic data and improving the interpretation of analytical results. In this study, we conducted a preliminary investigation using various visualization techniques to explore endogenous biases. Our findings demonstrate the potentials of visual analytics to uncover hidden biases and identify associated issues. Additionally, we synthesized these visualization strategies into a framework that approximates a method for detecting endogenous biases. Through this work, we advocate for the integration of visualization at three critical stages of spatial analysis in order to minimize errors, address ethical concerns, and reduce misinterpretations associated with endogenous biases.

This study investigates the variability of the Central England Temperature (CET) series in relation to the North Atlantic Oscillation (NAO) and the Pacific Decadal Oscillation (PDO) using advanced time series modeling techniques. Leveraging the world's longest continuous instrumental temperature dataset (1723-2023), this research applies ARIMA and ARIMAX models to quantify the impact of climatic oscillations on regional temperature variability, while also accounting for long-term warming trends. Spectral and coherence analyses further explore the periodic interactions between CET and the oscillations. Results reveal that NAO exerts a stronger influence on CET variability compared to PDO, with significant coherence observed at cycles of 5 to 7.5 years and 2 to 2.5 years for NAO, while PDO shows no statistically significant coherence. The ARIMAX model effectively captures both the upward warming trend and the influence of climatic oscillations, with robust diagnostics confirming its reliability. This study contributes to understanding the interplay between regional temperature variability and large-scale climatic drivers, providing a framework for future research on climatic oscillations and their role in shaping regional climate dynamics. Limitations and potential future directions, including the integration of additional climatic indices and comparative regional analyses, are also discussed.

In this work, we have implemented an accurate machine-learning approach for predicting various key analog and RF parameters of Negative Capacitance Field-Effect Transistors (NCFETs). Visual TCAD simulator and the Python high-level language were employed for the entire simulation process. However, the computational cost was found to be excessively high. The machine learning approach represents a novel method for predicting the effects of different sources on NCFETs while also reducing computational costs. The algorithm of an artificial neural network can effectively predict multi-input to single-output relationships and enhance existing techniques. The analog parameters of Double Metal Double Gate Negative Capacitance FETs (D2GNCFETs) are demonstrated across various temperatures ($T$), oxide thicknesses ($T_{ox}$), substrate thicknesses ($T_{sub}$), and ferroelectric thicknesses ($T_{Fe}$). Notably, at $T=300K$, the switching ratio is higher and the leakage current is $84$ times lower compared to $T=500K$. Similarly, at ferroelectric thicknesses $T_{Fe}=4nm$, the switching ratio improves by $5.4$ times compared to $T_{Fe}=8nm$. Furthermore, at substrate thicknesses $T_{sub}=3nm$, switching ratio increases by $81\%$ from $T_{sub}=7nm$. For oxide thicknesses at $T_{ox}=0.8nm$, the ratio increases by $41\%$ compared to $T_{ox}=0.4nm$. The analysis reveals that $T_{Fe}=4nm$, $T=300K$, $T_{ox}=0.8nm$, and $T_{sub}=3nm$ represent the optimal settings for D2GNCFETs, resulting in significantly improved performance. These findings can inform various applications in nanoelectronic devices and integrated circuit (IC) design.

We explore the dissipative phase transition of the two-photon Dicke model, a topic that has garnered significant attention recently. Our analysis reveals that while single-photon loss does not stabilize the intrinsic instability in the model, the inclusion of two-photon loss restores stability, leading to the emergence of superradiant states which coexist with the normal vacuum states. Using a second-order cumulant expansion for the photons, we derive an analytical description of the system in the thermodynamic limit which agrees well with the exact calculation results. Additionally, we present the Wigner function for the system, shedding light on the breaking of the Z4-symmetry inherent in the model. These findings offer valuable insights into stabilization mechanisms in open quantum systems and pave the way for exploring complex nonlinear dynamics in two-photon Dicke models.

Atom interferometers offer exceptional sensitivity to ultra-light dark matter (ULDM) through their precise measurement of phenomena acting on atoms. While previous work has established their capability to detect scalar and vector ULDM, their potential for detecting spin-2 ULDM remains unexplored. This work investigates the sensitivity of atom interferometers to spin-2 ULDM by considering several frameworks for massive gravity: a Lorentz-invariant Fierz-Pauli case and two Lorentz-violating scenarios. We find that coherent oscillations of the spin-2 ULDM field induce a measurable phase shift through three distinct channels: coupling of the scalar mode to atomic energy levels, and vector and tensor effects that modify the propagation of atoms and light. Atom interferometers uniquely probe all of these effects, while providing sensitivity to a different mass range from laser interferometers. Our results demonstrate the potential of atom interferometers to advance the search for spin-2 dark matter through accessing unexplored parameter space and uncovering new interactions between ULDM and atoms.

In this note, we define material-uniform hyperelastic bodies (in the sense of Noll) containing discrete disclinations and dislocations, and study their properties. We show in a rigorous way that the size of a disclination is limited by the symmetries of the constitutive relation; in particular, if the symmetry group of the body is discrete, it cannot admit arbitrarily small, yet non-zero, disclinations. We then discuss the application of these observations to the derivations of models of bodies with continuously-distributed defects.

Color centers in diamond, such as the GeV center, are promising candidates for quantum-based applications. Here, we investigate the impact of strain on the zero-phonon line (ZPL) position of GeV$^0$. Both hydrostatic and linear strain are modeled using density functional theory for GeV$^0$ concentrations of $1.61$ \% down to $0.10$ \%. We present qualitative and quantitative differences between the two strain types: for hydrostatic tensile and compressive strain, red-and blue-shifted ZPL positions are expected, respectively, with a linear relation between the ZPL shift and the experienced stress. By calculating the ZPL shift for varying GeV$^0$ concentrations, a shift of $0.15$ nm/GPa ($0.38$ meV/GPa) is obtained at experimentally relevant concentrations using a hybrid functional. In contrast, only red-shifted ZPL are found for tensile and compressive linear strain along the $\langle100\rangle$ direction. The calculated ZPL shift exceeds that of hydrostatic strain by at least one order of magnitude, with a significant difference between tensile and compressive strains: $3.2$ and $4.8$ nm/GPa ($8.1$ and $11.7$ meV/GPa), respectively. In addition, a peak broadening is expected due to the lifted degeneracy of the GeV$^0$ $e_g$ state, calculated to be about $6$ meV/GPa. These calculated results are placed in perspective with experimental observations, showing values of ZPL shifts and splittings of comparable magnitude.

Discrete-step walks describe the dynamics of particles in a lattice subject to hopping or splitting events at discrete times. Despite being of primordial interest to the physics of quantum walks, the topological properties arising from their discrete-step nature have been hardly explored. Here we report the observation of topological phases unique to discrete-step walks. We use light pulses in a double-fibre ring setup whose dynamics maps into a two-dimensional lattice subject to discrete splitting events. We show that the number of edge states is not simply described by the bulk invariants of the lattice (i.e., the Chern number and the Floquet winding number) as would be the case in static lattices and in lattices subject to smooth modulations. The number of edge states is also determined by a topological invariant associated to the discrete-step unitary operators acting at the edges of the lattice. This situation goes beyond the usual bulk-edge correspondence and allows manipulating the number of edge states without the need to go through a gap closing transition. Our work opens new perspectives for the engineering of topological modes for particles subject to quantum walks.

Topological insulators are characterized by insulating bulk states and robust metallic surface states. Band inversion is a hallmark of topological insulators: at time-reversal invariant points in the Brillouin zone, spin-orbit coupling (SOC) induces a swapping of orbital character at the bulk band edges. In this work, we develop a novel method to detect band inversion within continuum quantum Monte Carlo (QMC) methods that can accurately treat the electron correlation and spin-orbit coupling crucial to the physics of topological insulators. Our approach applies a momentum-space-resolved atomic population analysis throughout the first Brillouin zone utilizing the L\"owdin method and the one-body reduced density matrix produced with Diffusion Monte Carlo (DMC). We integrate this method into QMCPACK, an open source ab initio QMC package, so that these ground state methods can be used to complement experimental studies and validate prior DFT work on predicting the band structures of correlated topological insulators. We demonstrate this new technique on the topological insulator bismuth telluride, which displays band inversion between its Bi-p and Te-p states at the $\Gamma$-point. We show an increase in charge on the bismuth p orbital and a decrease in charge on the tellurium p orbital when comparing band structures with and without SOC. Additionally, we use our method to compare the degree of band inversion present in monolayer Bi$_2$Te$_3$, which has no interlayer van der Waals interactions, to that seen in the bulk. The method presented here will enable future, many-body studies of band inversion that can shed light on the delicate interplay between correlation and topology in correlated topological materials.

It has been shown that randomly formed communities in topological networks reduce the robustness of connectivity. However, in spatial networks, where community structures are not random but constrained by physical and geographical factors, the effect of these structures on the robustness is unclear. This paper investigates the emergence of local communities in road and communication networks, whose nodes are located by population data of major urban areas in Japan, and connected shortly with low cost. We show that, as the strength of community increases, the spatial networks become more vulnerable to both intentional attacks and random failures. As an application point of view, this result suggests that the densely setting nodes of equipments should be avoided under short links in constructing a spatial network. These findings contribute to understanding the important relation between local communities and the robustness of connectivity against attacks and disasters especially in spatial networks.

Climate change is intensifying rainfall extremes, making high-resolution precipitation projections crucial for society to better prepare for impacts such as flooding. However, current Global Climate Models (GCMs) operate at spatial resolutions too coarse for localized analyses. To address this limitation, deep learning-based statistical downscaling methods offer promising solutions, providing high-resolution precipitation projections with a moderate computational cost. In this work, we introduce a bias-informed conditional diffusion model for statistical downscaling of precipitation. Specifically, our model leverages a conditional diffusion approach to learn distribution priors from large-scale, high-resolution precipitation datasets. The long-tail distribution of precipitation poses a unique challenge for training diffusion models; to address this, we apply gamma correction during preprocessing. Additionally, to correct biases in the downscaled results, we employ a guided-sampling strategy to enhance bias correction. Our experiments demonstrate that the proposed model achieves highly accurate results in an 8 times downscaling setting, outperforming previous deterministic methods. The code and dataset are available at https://github.com/RoseLV/research_super-resolution

Analytical results are presented for the structure of networks that evolve via a preferential-attachment-random-deletion (PARD) model in the regime of overall network growth and in the regime of overall contraction. The phase transition between the two regimes is studied. At each time step a node addition and preferential attachment step takes place with probability $P_{\rm add}$, and a random node deletion step takes place with probability $P_{\rm del} = 1 - P_{\rm add}$. The balance between growth and contraction is captured by the parameter $\eta = P_{\rm add} - P_{\rm del}$, which in the regime of overall network growth satisfies $0 < \eta \le 1$ and in the regime of overall network contraction $-1 \le \eta < 0$. Using the master equation and computer simulations we show that for $-1 < \eta < 0$ the time-dependent degree distribution $P_t(k)$ converges towards a stationary form $P_{\rm st}(k)$ which exhibits an exponential tail. This is in contrast with the power-law tail of the stationary degree distribution obtained for $0 < \eta \le 1$. Thus, the PARD model has a phase transition at $\eta=0$, which separates between two structurally distinct phases. At the transition, for $\eta=0$, the degree distribution exhibits a stretched exponential tail. While the stationary degree distribution in the phase of overall growth represents an asymptotic state, in the phase of overall contraction $P_{\rm st}(k)$ represents an intermediate asymptotic state of a finite life span, which disappears when the network vanishes.

Temperature rise of qubits due to heating is a critical issue in large-scale quantum computers based on quantum-dot (QD) arrays. This leads to shorter coherence times, induced readout errors, and increased charge noise. Here, we propose a simple thermal circuit model to describe the heating effect on silicon QD array structures. Noting that the QD array is a periodic structure, we represent it as a thermal distributed-element circuit, forming a thermal transmission line. We validate this model by measuring the electron temperature in a QD array device using Coulomb blockade thermometry, finding that the model effectively reproduces experimental results. This simple and scalable model can be used to develop the thermal design of large-scale silicon-based quantum computers.

We perform a detailed Lie symmetry analysis for the hyperbolic system of partial differential equations that describe the one-dimensional Shallow Water magnetohydrodynamics equations within a rotating reference frame. We consider a relaxing condition $\mathbf{\mathbf{\nabla }}\left( h\mathbf{B} \right) \neq 0$ for the one-dimensional problem, which has been used to overcome unphysical behaviors. The hyperbolic system of partial differential equations depends on two parameters: the constant gravitational potential $g$ and the Coriolis term $f_{0}$, related to the constant rotation of the reference frame. For four different cases, namely $g=0,~f_{0}=0$; $g\neq 0\,,~f_{0}=0$; $g=0$, $f_{0}\neq 0$; and $g\neq 0$, $f_{0}\neq 0$ the admitted Lie symmetries for the hyperbolic system form different Lie algebras. Specifically the admitted Lie algebras are the $L^{10}=\left\{ A_{3,3}\rtimes A_{2,1}\right\} \otimes _{s}A_{5,34}^{a}$; $% L^{8}=A_{2,1}\rtimes A_{6,22}$; $L^{7}=A_{3,5}\rtimes\left\{ A_{2,1}\rtimes A_{2,1}\right\} $; and $L^{6}=A_{3,5}\rtimes A_{3,3}~$respectively, where we use the Morozov-Mubarakzyanov-Patera classification scheme. For the general case where $f_{0}g\neq 0$, we derive all the invariants for the Adjoint action of the Lie algebra $L^{6}$ and its subalgebras, and we calculate all the elements of the one-dimensional optimal system. These elements are then considered to define similarity transformations and construct analytic solutions for the hyperbolic system.

A universal numerical method is developed for the investigation of magnetic neutron scattering. By applying the pseudospectral-time-domain (PSTD) algorithm to the spinor version of the Schr\"odinger equation, the evolution of the spin-state of the scattered wave can be solved in full space and time. This extra spin degree of freedom brings some unique new features absent in the numerical theory on the scalar wave scatterings [1]. Different numerical stability condition has to be re-derived due to the coupling between the different spin states. As the simplest application, the neutron scattering by the magnetic field of a uniformly magnetized sphere is studied. The PSTD predictions are compared with those from the Born-approximation. This work not only provides a systematic tool for analyzing spin-matter interactions, but also builds the forward model for testing novel neutron imaging methodologies, such as the newly developed thermal neutron Fourier-transform ghost imaging.

Non-reciprocal hopping induces a synthetic magnetic flux which leads to the non-Hermitian Aharonov-Bohm effect. Since non-Hermitian Hamiltonians possess both real and imaginary eigenvalues, this effect allows the observation of real and imaginary persistent currents in a ring threaded by the synthetic flux~\cite{nrh8}. Motivated by this, we investigate the behavior of persistent currents in a disordered Hatano-Nelson ring with anti-Hermitian intradimer hopping. The disorder is diagonal and we explore three distinct models, namely the Aubry-Andr\'{e}-Harper model, the Fibonacci model, both representing correlated disorder, and an uncorrelated (random) model. We conduct a detailed analysis of the energy spectrum and examine the real and imaginary parts of the persistent current under various conditions such as different ring sizes and filling factors. Interestingly, we find that real and imaginary persistent currents exhibit amplification in the presence of correlated disorder. This amplification is also observed in certain individual random configurations but vanishes after configuration averaging. Additionally, we observe both diamagnetic and paramagnetic responses in the current behavior and investigate aspects of persistent currents in the absence of disorder that have not been previously explored. Interestingly, we find that the intradimer bonds host only imaginary currents, while the interdimer bonds carry only real currents.

The observation of the kilonova AT2017gfo and investigations of its light curves and spectra confirmed that neutron star mergers are sites of r-process nucleosynthesis. However, the identification of elements responsible for the spectral features is still challenging, particularly at the near-infrared wavelengths. In this study, we systematically searched for all possible near-infrared transitions of heavy elements using experimentally calibrated energy levels. Our analysis reveals that most candidate elements with strong absorption lines are lanthanides (Z=57-71) and actinides (Z=89-103). This is due to their complex structures leading to many low-lying energy levels, which results in strong transitions in the near-infrared range. Domoto et al. (2022) have shown that La III and Ce III can explain the absorption features at $\lambda\sim$ 12,000 - 15,000 A. While our results confirm that these two elements show strong infrared features, we additionally identify Gd III as the next most promising species. Due to its unique atomic structure involving the half-filled 4f and the outer 5d orbitals, Gd III has one of the lowest-lying energy levels, between which relatively strong transitions occur. We also find absorption lines caused by Gd III in the near-infrared spectrum of a chemically peculiar star HR 465, which supports their emergence in kilonova spectra. By performing radiative transfer simulations, we confirm that Gd III lines affect the feature at $\sim$ 12,000 A previously attributed to La III. Future space-based time-series observations of kilonova spectra will allow the identification of Gd III lines.

Recently, the joint design of optical systems and downstream algorithms is showing significant potential. However, existing rays-described methods are limited to optimizing geometric degradation, making it difficult to fully represent the optical characteristics of complex, miniaturized lenses constrained by wavefront aberration or diffraction effects. In this work, we introduce a precise optical simulation model, and every operation in pipeline is differentiable. This model employs a novel initial value strategy to enhance the reliability of intersection calculation on high aspherics. Moreover, it utilizes a differential operator to reduce memory consumption during coherent point spread function calculations. To efficiently address various degradation, we design a joint optimization procedure that leverages field information. Guided by a general restoration network, the proposed method not only enhances the image quality, but also successively improves the optical performance across multiple lenses that are already in professional level. This joint optimization pipeline offers innovative insights into the practical design of sophisticated optical systems and post-processing algorithms. The source code will be made publicly available at https://github.com/Zrr-ZJU/Successive-optimization

This Chapter examines the dynamics of conflict and collaboration in human-machine systems, with a particular focus on large-scale, internet-based collaborative platforms. While these platforms represent successful examples of collective knowledge production, they are also sites of significant conflict, as diverse participants with differing intentions and perspectives interact. The analysis identifies recurring patterns of interaction, including serial attacks, reciprocal revenge, and third-party interventions. These microstructures reveal the role of experience, cultural differences, and topic sensitivity in shaping human-human, human-machine, and machine-machine interactions. The chapter further investigates the role of algorithmic agents and bots, highlighting their dual nature: they enhance collaboration by automating tasks but can also contribute to persistent conflicts with both humans and other machines. We conclude with policy recommendations that emphasize transparency, balance, cultural sensitivity, and governance to maximize the benefits of human-machine synergy while minimizing potential detriments.

According to Albert Einstein, gravitation is analogous to an optical medium. Building on this idea, various definitions of the gradient-index (GRIN) medium representing curved spacetime have been proposed; often, these approaches demand advanced knowledge of General Relativity and its associated mathematical methods. This paper introduces a novel approach for generating the form of GRIN media that reproduces the behavior of light in the presence of a Schwarzschild black hole or about the equatorial spacetime of a Kerr black hole. Our approach is based on Noether's theorem that leads to optical Fermat's principle, leveraging the system's symmetry, resulting in a general method for finding GRIN media in a flat spacetime that reproduces the behavior of light in Schwarzschild-like spacetimes where only using photon trajectories as input. This approach is also valid for material particles and provides a complementary understanding of General Relativity.

We analyze the French housing market prices in the period 1970-2022, with high-resolution data from 2018 to 2022. The spatial correlation of the observed price field exhibits logarithmic decay characteristic of the two-dimensional random diffusion equation -- local interactions may create long-range correlations. We introduce a stylized model, used in the past to model spatial regularities in voting patterns, that accounts for both spatial and temporal correlations with reasonable values of parameters. Our analysis reveals that price shocks are persistent in time and their amplitude is strongly heterogeneous in space. Our study confirms and quantifies the diffusive nature of housing prices that was anticipated long ago (Clapp et al. 1994, Pollakowski et al. 1997), albeit on much restricted, local data sets.

In this study, we propose a data-driven, deep-learning-based Machine-Learning Symmetry Discovery (MLSD) algorithm to automate the discovery of continuous Lie group symmetries in classical mechanical systems from their time-evolution trajectory data. MLSD uses neural networks (NNs) to predict conserved physical quantities that implement symmetry transformations of the phase space coordinates. After training, MLSD is able to identify the Lie algebra, particularly non-abelian ones, as indicated by the Lie algebra structure coefficients. To demonstrate the effectiveness of the MLSD method, we applied it to simulated data from the classical three-dimensional Kepler problem and the harmonic oscillator. The results show that the algorithm successfully identified the hidden symmetry groups of both systems.

Assembling increasingly larger-scale defect-free optical tweezer-trapped atom arrays is essential for quantum computation and quantum simulations based on atoms. Here, we propose an AI-enabled, rapid, constant-time-overhead rearrangement protocol, and we experimentally assemble defect-free 2D and 3D atom arrays with up to 2024 atoms with a constant time cost of 60 ms. The AI model calculates the holograms for real-time atom rearrangement. With precise controls over both position and phase, a high-speed spatial light modulator moves all the atoms simultaneously. This protocol can be readily used to generate defect-free arrays of tens of thousands of atoms with current technologies, and become a useful toolbox for quantum error correction.

Dynamic resistance occurs in a superconducting tape carrying a dc transport current while being exposed to an alternating magnetic field. This effect is caused by flux movements interacting with the transport current. The dynamic resistance is already applied in many superconducting applications, for example superconducting flux pumps or persistent current switches. The resistance is highly dependent on the magnetic field and the frequency the superconductor is subjected to and its properties. When the dynamic resistance exceeds a certain value and thus enters the magnitude of the resistances of the normal conducting layers of the HTS tape, these normal conducting layers play a significant role in the total resistance of the tape. In this paper, modifications were made to the silver stabilizer and the total resistance of the HTS tape has been investigated. The experimental results with frequencies up to 1000 Hz and magnetic field up to 277 mT show significant increases in resistance. Additionally, a multilayer model based on H-formulation is presented to calculate the losses of the superconductor. The results also show significant heating due to the losses and therefore a temperature rise, which effects the measured total resistance. These results can be further used for applications where high switchable resistances are required with zero dc resistance when the magnet is turned off.

We study fractional Laplace motion (FLM) obtained from subordination of fractional Brownian motion to a gamma process, in the presence of an external drift that acts on the composite process or of an internal drift acting solely on the parental process. We derive the statistical properties of this FLM process and find that the external drift does not influence the mean-squared displacement (MSD), whereas the internal drift leads to normal diffusion, dominating at long times in the subdiffusive Hurst exponent regime. We also investigate the intricate properties of the probability density function (PDF), demonstrating that it possesses a central Gaussian region, whose expansion in time is influenced by FBM's Hurst exponent. Outside of this region the PDF follows a non-Gaussian pattern. The kurtosis of this FLM process converges toward the Gaussian limit at long times insensitive to the extreme non-Gaussian tails. Additionally, in the presence of the external drift, the PDF remains symmetric and centered at $x=vt$. In contrast, for the internal drift this symmetry is broken. The results of our computer simulations are fully consistent with the theoretical predictions. The FLM model is suitable for describing stochastic processes with a non-Gaussian PDF and long-ranged correlations of the motion.

The proposed Super Tau-Charm Facility (STCF) is an electron-positron collider designed to operate in a center-of-mass energy range from 2 to 7 GeV. It provides a unique platform for physics research in the tau-charm energy region. To fulfill the physics goals of STCF, high tracking efficiency and good momentum resolution is required for charged particles with momenta from 50 MeV/c to 3.5 GeV/c. A global track finding algorithm based on Hough transform has been developed and implemented in the STCF software framework to meet this requirement. The design of the algorithm and its performance with simulation are presented in this paper.

In this review article we summarize all experiments claiming quantum computational advantage to date. Our review highlights challenges, loopholes, and refutations appearing in subsequent work to provide a complete picture of the current statuses of these experiments. In addition, we also discuss theoretical computational advantage in example problems such as approximate optimization and recommendation systems. Finally, we review recent experiments in quantum error correction -- the biggest frontier to reach experimental quantum advantage in Shor's algorithm.

We explore pattern formation in an active fluid system involving two chemical species that regulate active stress: a fast-diffusing species ($A$) and a slow-diffusing species ($I$). The growth of species $A$ is modelled using a nonlinear logistic term. Through linear stability analysis, we derive phase diagrams illustrating the various dynamical regimes in parameter space. Our findings indicate that an increase in the P\'eclet number results in the destabilisation of the uniform steady state. In contrast, counter-intuitively, an increase in the nonlinear growth parameter of $A$ actually stabilises the homogeneous steady-state regime. Additionally, we observe that greater asymmetry between the species leads to three distinct dynamical phases, while low asymmetry fails to produce oscillatory instability. Numerical simulations conducted in instability regimes show patterns that range from irregular, arrhythmic configurations at high P\'eclet numbers to both transient and robust symmetry-breaking chimera states. Notably, these chimera patterns are more prevalent in the oscillatory instability regime, and our stability analysis indicates that this regime is the most extensive for high nonlinear growth parameters and moderately high P\'eclet numbers. Further, we also find soliton-like structures where aggregations of species $A$ merge, and new aggregations spontaneously emerge, and these patterns are prevalent in the phase of stationary instability. Overall, our study illustrates that a diverse array of patterns can emerge in active matter influenced by nonlinear growth in a chemical species, with chimeras being particularly dominant when the nonlinear growth parameter is elevated.

Strong correlation brings a rich array of emergent phenomena, as well as a daunting challenge to theoretical physics study. In condensed matter physics, the fractional quantum Hall effect is a prominent example of strong correlation, with Landau level mixing being one of the most challenging aspects to address using traditional computational methods. Deep learning real-space neural network wavefunction methods have emerged as promising architectures to describe electron correlations in molecules and materials, but their power has not been fully tested for exotic quantum states. In this work, we employ real-space neural network wavefunction techniques to investigate fractional quantum Hall systems. On both $1/3$ and $2/5$ filling systems, we achieve energies consistently lower than exact diagonalization results which only consider the lowest Landau level. We also demonstrate that the real-space neural network wavefunction can naturally capture the extent of Landau level mixing up to a very high level, overcoming the limitations of traditional methods. Our work underscores the potential of neural networks for future studies of strongly correlated systems and opens new avenues for exploring the rich physics of the fractional quantum Hall effect.

Differential top quark pair cross sections are measured in the dilepton final state as a function of kinematic variables associated to the dineutrino system. The measurements are performed making use of the Run 2 dataset collected by the CMS experiment at the CERN LHC collider, corresponding to proton-proton collisions recorded at center of mass energy of 13 TeV and an integrated luminosity of 138 fb$^{-1}$. The measured cross sections are found in agreement with theory predictions and Monte Carlo simulations of standard model processes.

Out-of-equilibrium electron-gas systems exhibit rich physics, which we explore through three problems. First, we study photoemission from metals, traditionally analyzed in the frequency domain. Unexpectedly, the photoemission rate oscillates at high frequencies as it decays, with the oscillation amplitude decaying faster than the average current. Analytical and numerical results reveal this behavior arises from interference between two excitation processes: one decaying via the Doniach-Sunjic power law and the other following the faster Nozi\`eres-De Dominicis law. XPS experiments targeting this feature could identify its frequency-domain counterpart. Second, we examine adiabaticity in an electron gas subject to a localized potential ramping up at a constant rate. Analytical and numerical findings map the parameter space where the system behaves adiabatically. Contrary to the Quantum Adiabatic Criterion, which links adiabaticity to slow ramp-up rates, we show that the number of energy scales involved in screening the potential dictates non-adiabaticity. Lastly, we investigate the collision of a neutral hydrogen atom with a copper surface. Electron transfer ionizes the H atom, activating an image-charge potential that pulls the ion toward the surface. Using a spinless model, we numerically track the atomic wave packets evolution and compute the sticking coefficient, the probability the atom remains near the surface. The coefficient peaks near 300 meV, balancing non-adiabatic contributions, which increase with energy, and the traversal time through the interaction region. Numerical results align semi-quantitatively with experimental data.

In recent years, complementary metal-oxide-semiconductor (CMOS) sensors have been demonstrated to have significant potential in X-ray astronomy, where long-term reliability is crucial for space X-ray telescopes. This study examines the long-term stability of a scientific CMOS sensor, focusing on its bias, dark current, readout noise, and X-ray spectral performance. The sensor was initially tested at -30 $^\circ$C for 16 months, followed by accelerated aging at 20 $^\circ$C. After a total aging period of 610 days, the bias map, dark current, readout noise, gain, and energy resolution exhibited no observable degradation. There are less than 50 pixels within the 4 k $\times$ 4 k array which show a decrease of the bias under 50 ms integration time by over 10 digital numbers (DNs). First-order kinetic fitting of the gain evolution predicts a gain degeneration of 0.73% over 3 years and 2.41% over 10 years. These results underscore the long-term reliability of CMOS sensors for application in space missions.

In 2017, as the solar cycle approached solar minimum, an unusually long and large depression was observed in galactic cosmic ray (GCR) protons, detected with the Alpha Magnetic Spectrometer (AMS-02), lasting for the second half of that year. The depression, as seen in the Bartel rotation-averaged proton flux, has the form of a Forbush decrease (FD). Despite this resemblance, however, the cause of the observed depression does not have such a simple explanation as FDs, due to coronal mass ejections (CMEs), typically last for a few days at 1 AU rather than half a year. In this work, we seek the cause of the observed depression and investigate two main possibilities. First, we consider a mini-cycle - a temporary change in the solar dynamo that changes the behavior of the global solar magnetic field and, by this, the modulation of GCRs. Secondly, we investigate the behavior of solar activity, both CMEs and co-rotating/stream interactions regions (C/SIRs), during this period. Our findings show that, although there is some evidence for mini-cycle behavior prior to the depression, the depression is ultimately due to a combination of recurrent CMEs, SIRs and CIRs. A particular characteristic of the depression is that the largest impacts that help to create and maintain it are due to four CMEs from the same, highly active, magnetic source that persists for several solar rotations. This active magnetic source is unusual given the closeness of the solar cycle to solar minimum, which also helps to make the depression more evident.

We propose a novel and systematic recurrence method for the energy spectra of non-Hermitian systems under open boundary conditions based on the recurrence relations of their characteristic polynomials. Our formalism exhibits better accuracy and performance on multi-band non-Hermitian systems than numerical diagonalization or the non-Bloch band theory. It also provides a targeted and efficient formulation for the non-Hermitian edge spectra. As demonstrations, we derive general expressions for both the bulk and edge spectra of multi-band non-Hermitian models with nearest-neighbor hopping and under open boundary conditions, such as the non-Hermitian Su-Schrieffer-Heeger and Rice-Mele models and the non-Hermitian Hofstadter butterfly - 2D lattice models in the presence of non-reciprocity and perpendicular magnetic fields, which is only made possible by the significantly lower complexity of the recurrence method. In addition, we use the recurrence method to study non-Hermitian edge physics, including the size-parity effect and the stability of the topological edge modes against boundary perturbations. Our recurrence method offers a novel and favorable formalism to the intriguing physics of non-Hermitian systems under open boundary conditions.

Photocurrent-induced harmonics appear in gases and solids due to tunnel ionization of electrons in strong fields and subsequent acceleration. In contrast to three-step harmonic emission, no return to the parent ions is necessary. Here we show that the same mechanism produces harmonics in metallic nanostructures in strong fields. Furthermore, we demonstrate how strong local field gradient, appearing as a consequence of the field enhancement, affects photocurrent-induced harmonics. This influence can shed light at the state of electron as it appears in the continuum, in particular, to its initial velocity.

Cs$_2$AgBiBr$_6$ shows promise for solution-processable optoelectronics, such as photovoltaics, photocatalysis and X-ray detection. However, various spectroscopic studies report rapid charge carrier mobility loss in the first picosecond after photoexcitation, limiting carrier collection efficiencies. The origin of this rapid mobility loss is still unclear. Here, we directly compare hot excitation with excitation over the indirect fundamental bandgap, using transient absorption and THz spectroscopy on the same Cs$_2$AgBiBr$_6$ thin film sample. From transient absorption spectroscopy, we find that hot carriers cool towards the band-edges with a cooling rate of 0.58 ps$^{-1}$, which coincides with the observed mobility loss rate from THz spectroscopy. Hence, our study establishes a direct link between the hot carrier cooling and ultrafast mobility loss on the picosecond timescale.

The transmission of light through an ensemble of two-level emitters in a one-dimensional geometry is commonly described by one of two emblematic models of quantum electrodynamics (QED): the driven-dissipative Dicke model or the Maxwell-Bloch equations. Both exhibit distinct features of phase transitions and phase separations, depending on system parameters such as optical depth and external drive strength. Here, we explore the crossover between these models via a parent spin model from bidirectional waveguide QED, by varying positional disorder among emitters. Solving mean-field equations and employing a second-order cumulant expansion for the unidirectional model -- equivalent to the Maxwell-Bloch equations -- we study phase diagrams, the emitter's inversion, and transmission depending on optical depth, drive strength, and spatial disorder. We find in the thermodynamic limit the emergence of phase separation with a critical value that depends on the degree of spatial order but is independent of inhomogeneous broadening effects. Even far from the thermodynamic limit, this critical value marks a special point in the emitter's correlation landscape of the unidirectional model and is also observed as a maximum in the magnitude of inelastically transmitted photons. We conclude that a large class of effective one-dimensional systems without tight control of the emitter's spatial ordering can be effectively modeled using a unidirectional waveguide approach.

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment aimed at determining the neutrino mass hierarchy and the CP-violating phase. The DUNE physics program also includes the detection of astrophysical neutrinos and the search for signatures beyond the Standard Model, such as nucleon decays. DUNE consists of a near detector complex located at Fermilab and four 17 kton Liquid Argon Time Projection Chamber (LArTPC) far detector modules to be built 1.5 km underground at SURF, approximately 1300 km away. The detectors are exposed to a wideband neutrino beam generated by a 1.2 MW proton beam with a planned upgrade to > 2 MW. Two 770 ton LArTPCs (ProtoDUNEs) have been operated at CERN for over 2 years as a testbed for DUNE far detectors and have been optimized to take new cosmic and test-beam data in 2024-2025. The DUNE and ProtoDUNE experiments and physics goals, as well as recent progress and results, are presented.

Satellite to ground quantum communication typically operates at night to reduce background signals, however it remains susceptible to noise from light pollution of the night sky. In this study we compare several methodologies for determining whether a Quantum Ground Station (QGS) site is viable for exchanging quantum signals with the upcoming Quantum Encryption and Science Satellite (QEYSSat) mission. We conducted ground site characterization studies at three locations in Canada: Waterloo, Ontario, Calgary, Alberta, and Priddis, Alberta. Using different methods we estimate the background counts expected to leak into the satellite-ground quantum channel, and determined whether the noise levels could prevent a quantum key transfer. We also investigate how satellite data recorded from the Visible Infrared Imaging Radiometer Suite (VIIRS) can help estimate conditions of a particular site, and find reasonable agreement with the locally recorded data. Our results indicate that the Waterloo, Calgary, and Priddis QGS sites should allow both quantum uplinks and downlinks with QEYSSat, despite their proximity to urban centres. Furthermore, our approach allows the use of satellite borne instrument data (VIIRS) to remotely and efficiently determine the potential of a ground site.

A new axion-haloscope is setup at the Johannes Gutenberg university of Mainz, named the Supax (a SUPerconducting AXion search) experiment. This setup is used to characterize the behaviour of a NbN coated superconducting cavity in a 2.5T strong magnetic field, at a resonance frequency of 8.4GHz. We observe an increasing surface resistance with increasing magnetic field, leading to a decreasing quality factor. The behaviour is similar to that of previously studied cavities using Nb3Tn.

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions.

The physics programme of the LHCb experiment at the Large Hadron Collider requires an efficient and precise reconstruction of the particle collision vertices. The LHCb Upgrade detector relies on a fully software-based trigger with an online reconstruction rate of 30 MHz, necessitating fast vertex finding algorithms. This paper describes a new approach to vertex reconstruction developed for this purpose. The algorithm is based on cluster finding within a histogram of the particle trajectory projections along the beamline and on an adaptive vertex fit. Its implementations and optimisations on x86 and GPU architectures and its performance on simulated samples are also discussed.

We propose and implement a comprehensive quantum compilation toolkit for solving the maximum independent set (MIS) problem on quantum hardware based on Rydberg atom arrays. Our end-to-end pipeline involves three core components to efficiently map generic MIS instances onto Rydberg arrays with unit-disk connectivity, with modules for graph reduction, hardware compatibility checks, and graph embedding. The first module (reducer) provides hardware-agnostic and deterministic reduction logic that iteratively reduces the problem size via lazy clique removals. We find that real-world networks can typically be reduced by orders of magnitude on sub-second time scales, thus significantly cutting down the eventual load for quantum devices. Moreover, we show that reduction techniques may be an important tool in the ongoing search for potential quantum speedups, given their ability to identify hard problem instances. In particular, for Rydberg-native MIS instances, we observe signatures of an easy-hard-easy transition and quantify a critical degree indicating the onset of a hard problem regime. The second module (compatibility checker) implements a hardware compatibility checker that quickly determines whether or not a given input graph may be compatible with the restrictions imposed by Rydberg quantum hardware. The third module (embedder) describes hardware-efficient graph embedding routines to generate (approximate) encodings with controllable overhead and optimized ancilla placements. We exemplify our pipeline with experiments run on the QuEra Aquila device available on Amazon Braket. In aggregate, our work provides a set of tools that extends the class of problems that can be tackled with near-term Rydberg atom arrays.

In socioeconomic systems, nonequilibrium dynamics naturally stem from the generically non-reciprocal interactions between self-interested agents, whereas equilibrium descriptions often only apply to scenarios where individuals act with the common good in mind. We bridge these two contrasting paradigms by studying a Sakoda-Schelling occupation model with both individualistic and altruistic agents, who, in isolation, follow nonequilibrium and equilibrium dynamics respectively. We investigate how the relative fraction of these two populations impacts the behavior of the system. In particular, we find that when fluctuations in the agents' decision-making process are small (high rationality), a very moderate amount of altruistic agents mitigates the sub-optimal concentration of individualists in dense clusters. In the regime where fluctuations carry more weight (low rationality), on the other hand, altruism progressively allows the agents to coordinate in a way that is significantly more robust, which we understand by reducing the model to a single effective population studied through the lens of active matter physics. We highlight that localizing the altruistic intervention at the right point in space may be paramount for its effectiveness.

Exciton mediated superconductor is a fascinating quantum phase of matter that occurs when excitons become the dominant excitation in materials, which is also very promising for high temperature superconductor. However, there is no experimental report of exciton mediated superconductivity. Herein, we realize exciton mediated superconductivity from exciton insulator, where the onset transition temperature can reach larger than 200 K. More profoundly, Bose-Einstein condensation (BEC) of exciton can facilitate the formation of exciton insulator and a transition happened from extremely high resistivity of 107 {\Omega} at 153.5 K to 100 {\Omega} at 125 K, indicating a superconducting transition. The resistance of exciton mediated superconductivity can not be absolutely reach zero possibly as a result of drag effect between electrons/holes and excitons. We reveal one rule for exciton mediated superconductivity is that the resistivity is an inverse linear function of the current through the BEC superfluid state, which should be ascribed to the Andreev-Bashkin effect, which reflects the coupling between the BEC state of exciton and Cooper pairs mediated by excitons. Furthermore, a record transition temperature above 200 K has been found for one superconducting sample, which shows the Josephson oscillation dominated by a pendulum-like equation caused by the quantum coupling between superconductor and BEC superfluid of exciton.

We address the challenge of incorporating non-Markovian electronic friction effects in quantum-mechanical approximations of dynamical observables. A generalized Langevin equation (GLE) is formulated for ring-polymer molecular dynamics (RPMD) rate calculations, which combines electronic friction with a description of nuclear quantum effects (NQEs) for adsorbates on metal surfaces. An efficient propagation algorithm is introduced that captures both the spatial dependence of friction strength and non-Markovian frictional memory. This framework is applied to a model of hydrogen diffusing on Cu(111) derived from ab initio density functional theory (DFT) calculations, revealing significant alterations in rate constants and tunnelling crossover temperatures due to non-Markovian effects. Our findings explain why previous classical molecular dynamics simulations with Markovian friction showed unexpectedly good agreement with experiment, highlighting the critical role of non-Markovian effects in first-principles atomistic simulations.

Brownian motion is essential for describing diffusion in systems ranging from simple to complex liquids. Unlike simple liquids, which consist of only a solvent, complex liquids, such as colloidal suspensions or the cytoplasm of a cell, are mixtures of various constituents with different shapes and sizes. Describing Brownian motion in such multiscale systems is extremely challenging because direct and many-body hydrodynamic interactions (and their interplay) play a pivotal role. Diffusion of small particles is mainly governed by a low viscous character of the solution, whereas large particles experience a highly viscous flow of the complex liquid on the macro scale. A quantity that encodes hydrodynamics on both length scales is the wave-vector-dependent viscosity. Assuming this quantity to be known -- in contrast to most studies in which the solvent shear viscosity is given -- provides a new perspective on studying the diffusivity of a tracer, especially in situations where the tracer size can vary by several orders of magnitude. Here, we start systematic studies of exact formal microscopic expressions for the short- and long-time self-diffusion coefficients of a single probe particle in a complex liquid in terms of short-ranged hydrodynamic response kernels. We study Brownian motion as a function of the probe size, contrasting most theories that focus on self-diffusion as a function of the crowder volume fraction. We discuss the limits of small and large probe sizes for various levels of approximations in our theory, and discuss the current successes and shortcomings of our approach.

Categorical symmetries have recently been shown to generalize the classification of phases of matter, significantly broadening the traditional Landau paradigm. To test these predictions, we propose a simple spin chain model that encompasses all gapped phases and second-order phase transitions governed by the categorical symmetry $\mathsf{Rep}(D_8)$. This model not only captures the essential features of non-invertible phases but is also straightforward enough to enable practical realization. Specifically, we outline an implementation using neutral atoms trapped in optical tweezer arrays. Employing a dual-species setup and Rydberg blockade, we propose a digital simulation approach that can efficiently implement the many-body evolution in several nontrivial quantum phases.

We derive the nonequilibrium conductance matrix for open stationary Chemical Reaction Networks (CRNs) described by a deterministic mass action kinetic equation. As an illustration, we determine the nonequilibrium conductance matrix of a CRN made of two sub-networks, called chemical modules, in two different ways: First by computing the nonequilibrium conductances of the modules that are then serially connected. Second by computing directly the nonequilibrium conductance of the CRN directly. The two approaches coincide, as expected from our theory of thermodynamic circuits. We end by discussing the advantages of splitting a CRN into smaller chemical modules.

Linear measurement is an important class of measurements for sensing classical signals including gravitational wave (GW), dark matter, infrared ray, rotation rate, etc. In this Letter, we focus on multiparameter linear measurement and establish a general tradeoff relation that tightly constrains the fundamental quantum limits of two independent parameters in a monochromatic classical signal detected by any linear quantum device. Such a tradeoff relation is universal and fundamental for multiparameter linear measurement since arising from Heisenberg's uncertainty principle. Compared with the Holevo Cram\'er-Rao bound, our tradeoff bound can completely identify the dependence between the attainable precision limits on estimated parameters. The dependence becomes more obvious such that the individual precision can not simultaneously reach the quantum limit as the so-called incompatible coefficient rises. Eventually, we find a necessary condition under which an optimal measurement protocol can saturate the general tradeoff relation, and show that the measurement phase can be tuned for adjusting different precision weight. This result is related to many applications, particularly detuned GW sensors for searching post-merger remnants due to the direct relation between the detuned frequency and incompatible coefficient.

We report new constraints and sensitivities to heavy neutral leptons (HNLs) with transition magnetic moments, also known as dipole-portal HNLs. This is accomplished using data from the T2K ND280 near detector in addition to the projected three-year dataset of the upgraded ND280 detector. Dipole-portal HNLs have been extensively studied in the literature and offer a potential explanation for the $4.8\sigma$ MiniBooNE anomaly. To perform our analysis, we simulate HNL decays to $e^+e^-$ pairs in the gaseous time projection chambers of the ND280 detector and its upgrade. Recasting an ND280 search for mass-mixed HNLs, we find that ND280 data places world-leading constraints on dipole-portal HNLs in the 390-743\,{\rm MeV} mass range, disfavoring the region of parameter space favored by the MiniBooNE anomaly. The addition of three years of ND280 upgrade data will be able to disfavor the MiniBooNE solution at the $5 \sigma$ confidence level and extend the world-leading constraints to dipole-portal HNLs in the 148-860\,{\rm MeV} mass range. Our analysis suggests that ND280 data excludes dipole-portal HNLs as a solution to the MiniBooNE excess, motivating a dedicated search within the T2K collaboration and potentially highlighting the need for alternative explanations for the MiniBooNE anomaly.

Nuclear magnetic resonance (NMR) is a powerful spectroscopic technique that is sensitive to the local atomic structure of matter. Computational predictions of NMR parameters can help to interpret experimental data and validate structural models, and machine learning (ML) has emerged as an efficient route to making such predictions. Here, we systematically study graph-neural-network approaches to representing and learning tensor quantities for solid-state NMR -- specifically, the anisotropic magnetic shielding and the electric field gradient. We assess how the numerical accuracy of different ML models translates into prediction quality for experimentally relevant NMR properties: chemical shifts, quadrupolar coupling constants, tensor orientations, and even static 1D spectra. We apply these ML models to a structurally diverse dataset of amorphous SiO$_2$ configurations, spanning a wide range of density and local order, to larger configurations beyond the reach of traditional first-principles methods, and to the dynamics of the $\alpha\unicode{x2013}\beta$ inversion in cristobalite. Our work marks a step toward streamlining ML-driven NMR predictions for both static and dynamic behavior of complex materials, and toward bridging the gap between first-principles modeling and real-world experimental data.

In this paper, we present the numerical analysis and simulations of a multi-dimensional memristive device model. Memristive devices and memtransistors based on two-dimensional (2D) materials have demonstrated promising potential as components for next-generation artificial intelligence (AI) hardware and information technology. Our charge transport model describes the drift-diffusion of electrons, holes, and ionic defects self-consistently in an electric field. We incorporate two types of boundary models: ohmic and Schottky contacts. The coupled drift-diffusion partial differential equations are discretized using a physics-preserving Voronoi finite volume method. It relies on an implicit time-stepping scheme and the excess chemical potential flux approximation. We demonstrate that the fully discrete nonlinear scheme is unconditionally stable, preserving the free-energy structure of the continuous system and ensuring the non-negativity of carrier densities. Novel discrete entropy-dissipation inequalities for both boundary condition types in multiple dimensions allow us to prove the existence of discrete solutions. We perform multi-dimensional simulations to understand the impact of electrode configurations and device geometries, focusing on the hysteresis behavior in lateral 2D memristive devices. Three electrode configurations -- side, top, and mixed contacts -- are compared numerically for different geometries and boundary conditions. These simulations reveal the conditions under which a simplified one-dimensional electrode geometry can well represent the three electrode configurations. This work lays the foundations for developing accurate, efficient simulation tools for 2D memristive devices and memtransistors, offering tools and guidelines for their design and optimization in future applications.

Light-matter interaction with squeezed vacuum has received much interest for the ability to enhance the native interaction strength between an atom and a photon with a reservoir assumed to have an infinite bandwidth. Here, we study a model of parametrically driven cavity quantum electrodynamics (cavity QED) for enhancing light-matter interaction while subjected to a finite-bandwidth squeezed vacuum drive. Our method is capable of unveiling the effect of relative bandwidth as well as squeezing required to observe the anticipated anti-crossing spectrum and enhanced cooperativity without the ideal squeezed bath assumption. Furthermore, we analyze the practicality of said models when including intrinsic photon loss due to resonators imperfection. With these results, we outline the requirements for experimentally implementing an effectively squeezed bath in solid-state platforms such as InAs quantum dot cavity QED such that \textit{in situ} control and enhancement of light-matter interaction could be realized.

The high-luminosity phase of LHC operations (HL-LHC), will feature a large increase in simultaneous proton-proton interactions per bunch crossing up to 200, compared with a typical leveling target of 64 in Run 3. Such an increase will create a very challenging environment in which to perform charged particle trajectory reconstruction, a task crucial for the success of the ATLAS physics program, and will exceed the capabilities of the current ATLAS Inner Detector (ID). A new all-silicon Inner Tracker (ITk) will replace the current ID in time for the start of the HL-LHC. To ensure successful use of the ITk capabilities in Run 4 and beyond, the ATLAS tracking software has been successfully adapted to achieve state-of-the-art track reconstruction in challenging high-luminosity conditions with the ITk detector. This paper presents the expected tracking performance of the ATLAS ITk based on the latest available developments since the ITk technical design reports.

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino oscillation experiment aiming to measure the oscillation parameters with an unprecedented precision that will allow determining the CP violation phase in the leptonic sector and the neutrino mass ordering. The Far Detector of DUNE will consist of four 17 kton liquid argon Time Projection Chambers (LAr-TPC). Inside a LAr-TPC, a Photon Detection System (PDS) is needed to detect the scintillation light produced by the interacting particles. The PDS signal provides the interaction time for non-beam events and improves the calorimetric reconstruction. To validate DUNE technology, two large-scale prototypes, of 750 ton of LAr each, have been constructed at CERN, ProtoDUNE-HD and ProtoDUNE-VD. The PDS of both prototypes is based on the XArapuca concept, a SiPM-based device that provides good detection efficiency covering large surfaces at a reasonable cost. This document presents the preliminary performance of the ProtoDUNE-HD Photon Detection System, which has taken data from April to November 2024.

Realizing universal fault-tolerant quantum computation is a key goal in quantum information science. By encoding quantum information into logical qubits utilizing quantum error correcting codes, physical errors can be detected and corrected, enabling substantial reduction in logical error rates. However, the set of logical operations that can be easily implemented on such encoded qubits is often constrained, necessitating the use of special resource states known as 'magic states' to implement universal, classically hard circuits. A key method to prepare high-fidelity magic states is to perform 'distillation', creating them from multiple lower fidelity inputs. Here we present the experimental realization of magic state distillation with logical qubits on a neutral-atom quantum computer. Our approach makes use of a dynamically reconfigurable architecture to encode and perform quantum operations on many logical qubits in parallel. We demonstrate the distillation of magic states encoded in d=3 and d=5 color codes, observing improvements of the logical fidelity of the output magic states compared to the input logical magic states. These experiments demonstrate a key building block of universal fault-tolerant quantum computation, and represent an important step towards large-scale logical quantum processors.

Estimating the effective energy, $E_\text{eff}$ of a stationary probability distribution is a challenge for non-equilibrium steady states. Its solution could offer a novel framework for describing and analyzing non-equilibrium systems. In this work, we address this issue within the context of scalar active matter, focusing on the continuum field theory of Active Model B+. We show that the Wavelet Conditional Renormalization Group method allows us to estimate the effective energy of active model B+ from samples obtained by numerical simulations. We investigate the qualitative changes of $E_\text{eff}$ as the activity level increases. Our key finding is that in the regimes corresponding to low activity and to standard phase separation the interactions in $E_\text{eff}$ are short-ranged, whereas for strong activity the interactions become long-ranged and lead to micro-phase separation. By analyzing the violation of Fluctuation-Dissipation theorem and entropy production patterns, which are directly accessible within the WCRG framework, we connect the emergence of these long-range interactions to the non-equilibrium nature of the steady state. This connection highlights the interplay between activity, range of the interactions and the fundamental properties of non-equilibrium systems.