We report on the masses and hyperfine structure of ground and isomeric states in $^{114,116,118,120}$Ag isotopes, measured with the phase-imaging ion-cyclotron-resonance technique (PI-ICR) with the JYFLTRAP mass spectrometer and the collinear laser spectroscopy beamline at the Ion Guide Isotope Separator On-Line (IGISOL) facility, Jyv\"askyl\"a, Finland. We measured the masses and excitation energies, electromagnetic moments, and charge radii, and firmly established the nuclear spins of the long-lived states. A new isomer was discovered in $^{118}$Ag and the half-lives of $^{118}$Ag long-lived states were reevaluated. We unambiguously pinned down the level ordering of all long-lived states, placing the inversion of the $I = 0^-$ and $I = 4^+$ states at $A = 118$ $(N = 71)$. Lastly, we compared the electromagnetic moments of each state to empirical single-particle moments to identify the dominant configuration where possible.

The solar composition problem has puzzled astrophysicists for more than 20 years. Recent measurements of carbon-nitrogen-oxygen (CNO) neutrinos by the Borexino experiment show a $\sim2\sigma$ tension with the "low-metallicity" determinations. $^{14}$N$(p,\gamma)^{15}$O, the slowest reaction in the CNO cycle, plays a crucial role in the standard solar model (SSM) calculations of CNO neutrino fluxes. Here we report a direct measurement of the $^{14}$N$(p,\gamma)^{15}$O reaction, in which $S$-factors for all transitions were simultaneously determined in the energy range of $E_p=110-260$ keV for the first time. Our results resolve previous discrepancies in the ground-state transition, yielding a zero-energy $S$-factor $S_{114}(0) = 1.92\pm0.08$ keV b which is 14% higher than the $1.68\pm0.14$ keV b recommended in Solar Fusion III (SF-III). With our $S_{114}$ values, the SSM B23-GS98, and the latest global analysis of solar neutrino measurements, the C and N photospheric abundance determined by the Borexino experiment is updated to $N_{\mathrm{CN}}=({4.45}^{+0.69}_{-0.61})\times10^{-4}$. This new $N_{\mathrm{CN}}$ value agrees well with latest "high-metallicity" composition, however, is also consistent with the "low-metallicity" determination within $\sim 1\sigma$ C.L., indicating that the solar metallicity problem remains an open question. In addition, the significant reduction in the uncertainty of $S_{114}$ paves the way for the precise determination of the CN abundance in future large-volume solar neutrino measurements.

Neutrinoless double-beta decay ($0\nu\beta\beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the universe. The Large Enriched Germanium Experiment for Neutrinoless Double-Beta Decay (LEGEND) will operate in two phases to search for $0\nu\beta\beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in $^{76}$Ge to achieve a half-life sensitivity of 10$^{27}$ (10$^{28}$) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize Affinity Propagation to cluster waveform signals based on their shape and a Support Vector Machine to classify them into different categories. We train, optimize, test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum physics event sacrifice of $0.024 ^{+0.004}_{-0.003} \%$ when performing data cleaning cuts. Our model is being used to accelerate data cleaning development for LEGEND-200.

The precise measurement of parity-violating asymmetries in parity-violating electron scattering experiments is a powerful tool for probing new physics beyond the Standard Model. Achieving the expected precision requires both experimental and post-processing signal corrections. This includes using auxiliary detectors to distinguish the main signal from background signals and implementing post-measurement corrections, such as the Bayesian statistics method, to address uncontrolled factors during the experiments. Asymmetry values in the scattering of electrons off proton targets in QWeak and P2 and off electron targets in MOLLER are influenced by detector array configurations, beam polarization angles, and beam spin variations. The Bayesian framework refines full probabilistic models to account for all necessary factors, thereby extracting asymmetry values and the underlying physics under specified conditions. For the QWeak experiment, a reanalysis of the inelastic asymmetry measurement using the Bayesian method has yielded a closer fit to measured asymmetries, with uncertainties reduced by 40\% compared to the Monte Carlo minimization method. This approach was successfully applied to simulated data for the MOLLER experiment and is predicted to be similarly effective in P2.

In non-central heavy-ion collisions, the particle-emitting source can be tilted away from the beam direction, an effect that becomes particularly significant at collision energies of a few GeV and lower. This phenomenon, manifest itself in many observables such as directed flow, polarization, and vorticity, is therefore important to investigate. In this paper, we study the consistency between the tilt extracted directly from the freeze-out distribution of pions and the tilt parameter obtained using the azimuthally sensitive femtoscopy (asHBT) method. Using the UrQMD model, we demonstrate a strong dependence of the tilt parameter extracted with asHBT on the momentum of the particle pair. Considering the experimental challenges in accessing low particle momenta - where the tilt parameter extracted with asHBT closely matches the tilt of the freeze-out distribution of pions - we propose an exponential extrapolation method to obtain the tilt of the entire freeze-out distribution. This approach aims to enhance the accuracy of experimental measurements of tilt in non-central heavy-ion collisions.

Monte Carlo (MC) simulations, particularly using FLUKA, are essential for replicating real-world scenarios across scientific and engineering fields. Despite the robustness and versatility, FLUKA faces significant limitations in automation and integration with external post-processing tools, leading to workflows with a steep learning curve, which are time-consuming and prone to human errors. Traditional methods involving the use of shell and Python scripts, MATLAB, and Microsoft Excel require extensive manual intervention and lack flexibility, adding complexity to evolving scenarios. This study explores the potential of Large Language Models (LLMs) and AI agents to address these limitations. AI agents, integrate natural language processing with autonomous reasoning for decision-making and adaptive planning, making them ideal for automation. We introduce AutoFLUKA, an AI agent application developed using the LangChain Python Framework to automate typical MC simulation workflows in FLUKA. AutoFLUKA can modify FLUKA input files, execute simulations, and efficiently process results for visualization, significantly reducing human labor and error. Our case studies demonstrate that AutoFLUKA can handle both generalized and domain-specific cases, such as Microdosimetry, with an streamlined automated workflow, showcasing its scalability and flexibility. The study also highlights the potential of Retrieval Augmentation Generation (RAG) tools to act as virtual assistants for FLUKA, further improving user experience, time and efficiency. In conclusion, AutoFLUKA represents a significant advancement in automating MC simulation workflows, offering a robust solution to the inherent limitations. This innovation not only saves time and resources but also opens new paradigms for research and development in high energy physics, medical physics, nuclear engineering space and environmental science.

The effect of violation of $T$-invariance, provided that $P$-parity is preserved, is given by the total cross section of the interaction of a vector-polarized particle with a tensor-polarized target. A formalism for calculating this effect developed previously and based on the spin-dependent Glauber theory of elastic $pd$ scattering is used here to calculate the effect under discussion in the range of collision energies corresponding to the invariant mass of the $pN$ system $\sqrt{s_{pN}}=5$--$30$ GeV. The spin-dependent amplitudes of elastic $pN$ scattering required for this calculation are taken from existing phenomenological models for $pN$ scattering in the energy region considered.

Charge radii can be generally used to encode the information about various fine structures of finite nuclei. In this work, a constructed Bayesian neural network based on Monte Carlo dropout approach is proposed to accurately describe the charge radii of nuclei with proton number $Z\geq 20$ and mass number $A\geq 40$. More motivated underlying mechanisms are incorporated into this combined model except for the basic building blocks with the specific proton and neutron numbers, which naturally contains the pairing effect, isospin effect, shell closure effect associated to the Casten factor $P$, valence neutrons, valence protons, quadrupole deformation $\beta_{20}$, high order hexadecapole deformation $\beta_{40}$, and the local ``abnormal" shape staggering effect of $^{181,183,185}$Hg. To avoid the over-fitting puzzle along $Z=28$, $50$, and $82$ isotopic chains, the modified Casten factor $P^{*}$ is tentatively introduced into the input structure parameter sets. We have successfully demonstrated the ability of the Monte Carlo dropout Bayesian neural network (MC-dropout BNN) model to significantly increase the accuracy in predicting the nuclear charge radii. The standard root-mean-square deviation falls into $0.0084$ fm for the training data and $0.0124$ fm for the validation data with the modified Casten factor $P^{*}$ input. Meanwhile, the shell closure effect of nuclear charge radii can be reproduced remarkably well.

We present a brief report (at the Nufact-2024 conference) summarizing a global extraction of the ${\rm ^{12}C}$ longitudinal (${\cal R}_L$) and transverse (${\cal R}_T$) nuclear electromagnetic response functions from an analysis of all available electron scattering data on carbon. Since the extracted response functions cover a large kinematic range they can be readily used for comparison to theoretical predictions as well as validation and tuning Monte Carlo (MC) generators for electron and neutrino scattering experiments. Comparisons to several theoretical approaches and MC generators are given in detail in arXiv:2409.10637v1 [hep-ex]. We find that among all the theoretical models that were investigated, the ``Energy Dependent-Relativistic Mean Field'' (ED-RMF) approach provides the best description of both the Quasielastic (QE) and {\it nuclear excitation} response functions (leading to single nucleon final states) over all values of four-momentum transfer. The QE data are also well described by the "Short Time Approximation Quantum Monte Carlo" (STA-QMC) calculation which includes both single and two nucleon final states which presently is only valid for momentum transfer $\bf q > $ 0.3 GeV and does not include nuclear excitations. However, an analytic extrapolation of STA-QMC to lower $\bf q$ has been implemented in the GENIE MC generator for $\rm^{4}He$ and a similar extrapolation for ${\rm ^{12}C}$ is under development. Both approaches have the added benefit that the calculations are also directly applicable to the same kinematic regions for neutrino scattering. In addition we also report on a universal fit to all electron scattering data that can be used in lieu of experimental data for validation of Monte Carlo generators (and is in the process of being implemented in GENIE).

We propose a new method to investigate the existence and location of the conjectured high-temperature critical point of strongly interacting matter via contours of constant entropy density. By approximating these lines as a power series in the baryon chemical potential $\mu_B$, one can extrapolate them from first-principle results at zero net-baryon density, and use them to locate the QCD critical point, including the associated first-order and spinodal lines. As a proof of principle, we employ currently available continuum-extrapolated first-principle results from the Wuppertal--Budapest collaboration to find a critical point at a temperature and a baryon chemical potential of $T_c = 114.3 \pm 6.9$ MeV and $\mu_{B,c} = 602.1 \pm 62.1$ MeV, respectively. We advocate for a more precise determination of the required expansion coefficients via lattice QCD simulations as a means of pinpointing the location of the critical endpoint in the phase diagram of strongly interacting matter.