New Deep Learning Data Analysis Method for PROSPECT using GAPE: Genetic Algorithm Powered EvolutionWe propose a genetic algorithm powered evolution (GAPE) method to create deep learning solutions for energy and position estimation for reactor antineutrino interactions in the Precision Reactor…M. Adriamirado, A. B. Balantekin, C. Bass et al.·Apr 9, 2026SaveLearn
Fast and accurate noise removal by curve fitting using orthogonal polynomialsLocal polynomial smoothing is a widespread technique in data analysis, and Savitzky-Golay (SG) filters are one of its most well-known realizations. In real settings, the effectiveness of SG filtering…Andrea Gallo Rosso·Apr 8, 2026SaveLearn
Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteriesDiagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to…Malik Hassanaly, Corey R. Randall, Peter J. Weddle et al.·Apr 2, 2026SaveLearn
Growth-rate distributions at stationarityWe propose new analytical tools for describing growth-rate distributions generated by stationary time-series. Our analysis shows how deviations from normality are not pathological behaviour, as…Edgardo Brigatti·Mar 31, 2026SaveLearn
Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon SpectrometerThe identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This task will become…Jonathan Renusch·Mar 26, 2026SaveLearn
Chiral moments make chiral measuresWe develop a family of chiral measures to quantify the chirality of a distribution and assign it a handedness. Our measures are built using the tensorial moments of the distribution, which naturally…Emilio Pisanty, Nicola Mayer, Andrés Ordóñez et al.·Mar 25, 2026SaveLearn
Beyond the Central Limit: Universality of the Gamma Distribution from Padé-Enhanced Large DeviationsThe central limit theorem provides the theoretical foundation for the universality of the normal distribution: under broad conditions, the asymptotic distribution of a sum of independent random…Mario Castro, José A. Cuesta·Mar 24, 2026SaveLearn
Construction of the Global χ2 Function for the Simultaneous Fitting of Correlated Energy-Dependent Cross SectionsIn this paper, the global χ2 function for the simultaneous fitting of correlated energy-dependent cross sections is constructed, where the correlations between the measured cross sections of…Linquan Shao, Haoyu Yan, Yingjun Chen et al.·Mar 22, 2026SaveLearn
VecAmpFit: vectorized amplitude-analysis fitting libraryA new library VecAmpFit for multidimensional amplitude analyses in high-energy physics has been developed for an ongoing amplitude analysis at Belle II experiment. It includes a fitter performing…K. Chilikin·Mar 20, 2026SaveLearn
Automatic Termination Strategy of Inelastic Neutron-scattering Measurement Using Bayesian Optimization for Bin-width SelectionCurrently, an excessive amount of event data is being obtained in four-dimensional inelastic neutron-scattering experiments. A method for automatic bin-width optimization of multidimensional…Kensuke Muto, Hirotaka Sakamoto, Kenji Nagata et al.·Mar 16, 2026SaveLearn
A complex network approach to characterize clustering of events in irregular time seriesIn complex systems, events occur at irregular intervals that inherently encode the underlying dynamics of the system. Analyzing the temporal clustering of these events reveals critical insights into…Ambedkar Sanket Sukdeo, K. Shri Vignesh, Sachin S. Gunthe et al.·Mar 16, 2026SaveLearn
Recent advances and trends in pattern recognition and data analysis for RICH detectorsRing Imaging Cherenkov (RICH) detectors are a key component of particle identification systems in many particle, nuclear and astroparticle physics experiments. Their ultimate performance depends not…Luka Santelj·Mar 13, 2026SaveLearn
Classifying hadronic objects in ATLAS with ML/AI algorithmsThe identification of hadronic final states plays a crucial role in the physics programme of the ATLAS Experiment at the CERN LHC. Sophisticated artificial intelligence (AI) algorithms are employed…Leonardo Toffolin·Mar 12, 2026SaveLearn
Learning the Standard Model Manifold: Bayesian Latent Diffusion for Collider Anomaly DetectionWe propose a physics-informed anomaly detection framework for collider data based on a Bayesian latent diffusion model. Our method combines a probabilistic encoder with diffusion dynamics in the…Jigar Patel, Tommaso Dorigo·Mar 6, 2026SaveLearn
Structured generalized sliced Wasserstein distance for keV X-ray polarization analysis with Gas Pixel DetectorBecause of the special angular distribution of excited electrons by the photoelectric effect, the Gas Pixel Detector (GPD) is effective in measuring keV X-ray polarization of astrophysical events…Pengcheng Ai, Hongtao Qin, Xiangming Sun et al.·Mar 3, 2026SaveLearn
GNN For Muon Particle Momentum estimationDue to a high rate of overall data generation relative to data generation of interest, the CMS experiment at the Large Hadron Collider uses a combination of hardware- and software-based triggers to…Vishak K Bhat, Eric A. F. Reinhardt, Sergei Gleyzer·Mar 3, 2026SaveLearn
Data Unfolding: From Problem Formulation to Result AssessmentExperimental data in particle and nuclear physics, particle astrophysics, and radiation protection dosimetry are collected using experimental facilities that consist of a complex system of sensors,…Nikolay D. Gagunashvili·Mar 3, 2026SaveLearn
Titanic overconfidence -- dark uncertainty can sink hybrid metrology for semiconductor manufacturingHybrid metrology for semiconductor manufacturing is on a collision course with dark uncertainty. An IEEE technology roadmap for this venture has targeted a linewidth uncertainty of +/- 0.17 nm at 95…Ronald G. Dixson, Adam L. Pintar, R. Joseph. Kline et al.·Feb 26, 2026SaveLearn
Maximum Likelihood Particle Tracking in Turbulent Flows via Sparse OptimizationLagrangian particle tracking is essential for characterizing turbulent flows, but inferring particle acceleration from inherently noisy position data remains a significant challenge. Fluid particles…Griffin M Kearney, Kasey M Laurent, Makan Fardad·Feb 25, 2026SaveLearn
Symmetry-Constrained Forecasting of Periodically Correlated Energy ProcessesTime series in energy systems, such as solar irradiance, wind speed, or electrical load, are characterized by strong diurnal and seasonal periodicities. Accurate forecasting requires accounting for…Cyril Voyant, Candice Banes, Luis Garcia-Gutierrez et al.·Feb 21, 2026SaveLearn
Poisson Log-Normal Process for Count Data PredictionModeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detection events.…Anushka Saha, Abhijith Gandrakota, Alexandre V. Morozov·Feb 5, 2026SaveLearn
Comparison of Image Processing Models in Quark Gluon Jet ClassificationQuark-gluon discrimination provides a useful test case for studying how different machine-learning architectures learn the spatial structure of QCD radiation. In this work, we compare convolutional…Daeun Kim, Jaeyoon Cho, Jiwon Lee et al.·Jan 29, 2026SaveLearn
McSAS3: improved Monte Carlo small-angle scattering analysis software for dilute and dense scatterersMcSAS3 is the refactored successor to the original McSAS Monte Carlo small-angle scattering analysis software. It is intended to be integrated in automated data processing pipelines, but can also be…Brian Richard Pauw, Ingo Breßler·Jan 26, 2026SaveLearn
Learning to bin: differentiable and Bayesian optimization for multi-dimensional discriminants in high-energy physicsCategorizing events using discriminant observables is central to many high-energy physics analyses. Yet, bin boundaries are often chosen manually. A simple, popular choice in multi-classification…Johannes Erdmann, Nitish Kumar Kasaraguppe, Florian Mausolf·Jan 12, 2026SaveLearn
Neighbourhood topology unveils pathological hubs in the brain networks of epilepsy-surgery patientsPathological hubs in the brain networks of epilepsy patients are hypothesized to drive seizure generation and propagation. In epilepsy-surgery patients, these hubs have traditionally been associated…Leonardo Di Gaetano, Fernando A. N. Santos, Federico Battiston et al.·Jan 5, 2026SaveLearn