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 ClassificationWe present a comprehensive comparison of convolutional and transformer-based models for distinguishing quark and gluon jets using simulated jet images from Pythia 8. By encoding jet substructure into…Daeun Kim, Jiwon Lee, Wonjun Jeong 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 by hand. A simple, popular choice is to apply argmax…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
Applying Gaussian Mixture Models to Track Reconstruction in Inelastic Scattering Experiments with Active TargetsActive targets such as ACTAR TPC are well suited for studying giant resonances in unstable nuclei via inelastic scattering in inverse kinematics. A key challenge in such measurements is the detection…A. Arokiaraj, M. B. Latif, R. Raabe et al.·Dec 18, 2025SaveLearn
Machine LearningThis chapter gives an overview of the core concepts of machine learning (ML) -- the use of algorithms that learn from data, identify patterns, and make predictions or decisions without being…Javier M. Duarte, Uros Seljak, Kazu Terao·Dec 11, 2025SaveLearn
Automating High Energy Physics Data Analysis with LLM-Powered AgentsWe present a proof-of-principle study demonstrating the use of large language model (LLM) agents to automate a representative high energy physics (HEP) analysis. Using the Higgs boson diphoton…Eli Gendreau-Distler, Joshua Ho, Dongwon Kim et al.·Dec 8, 2025SaveLearn
Analysis framework for higher-order temporal correlations with applications to human heartbeatsWe propose a time series analysis framework focused on higher-order temporal correlations in the event sequence beyond the interevent time distribution by employing the burst-tree decomposition…Tibebe Birhanu, Hang-Hyun Jo·Dec 1, 2025SaveLearn
Coincidence Algebra Bundle for Decay Quivers: An Algebraic Approach to Gamma-ray SpectroscopyMotivated by the need for a more comprehensive algebraic structure to calculate coincidence probabilities of a general decay scheme for gamma ray spectroscopy, we model the decay scheme, rather…Liam Schmidt·Nov 26, 2025SaveLearn
Identifying statistical indicators of temporal asymmetry using a data-driven approachThe dynamics of time-reversible systems are statistically indistinguishable when observed forward or backward in time. A rich literature of statistical methods to distinguish irreversible dynamics…Teresa Dalle Nogare, Ben D. Fulcher·Nov 20, 2025SaveLearn
The Ensemble Kalman Inversion RaceEnsemble Kalman methods were initially developed to solve nonlinear data assimilation problems in oceanography, but are now popular in applications far beyond their original use cases. Of particular…Rebecca Gjini, Matthias Morzfeld, Oliver R. A. Dunbar et al.·Nov 19, 2025SaveLearn
Integral Bayesian symbolic regression for optimal discovery of governing equations from scarce and noisy dataUnderstanding how systems evolve over time often requires discovering the differential equations that govern their behavior. Automatically learning these equations from experimental data is…Oriol Cabanas-Tirapu, Sergio Cobo-Lopez, Savannah E. Sanchez et al.·Nov 18, 2025SaveLearn
Human-aligned Quantification of Numerical DataQuantifying numerical data involves addressing two key challenges: first, determining whether the data can be naturally quantified, and second, identifying the numerical intervals or ranges of values…Anton Kolonin·Nov 15, 2025SaveLearn
Reverse Stress Testing for Supply Chain ResilienceSupply chains' increasing globalization and complexity have recently produced unpredictable disruptions, ripple effects, and cascading resulting failures. Proposed practices for managing these…Madison Smith, Michael Gaiewski, Sam Dulin et al.·Nov 10, 2025SaveLearn
Response to Comment from Robert Cousins on Confidence intervals for the Poisson distributionRobert Cousins has posted a comment on my manuscript on ``Confidence intervals for the Poisson distribution''. His key point is that one should not include in the likelihood non-physical parameter…Frank C. Porter·Oct 29, 2025SaveLearn
Information-theoretic analysis of temporal dependence in discrete stochastic processes: Application to precipitation predictabilityUnderstanding the temporal dependence of precipitation is key to improving weather predictability and developing efficient stochastic rainfall models. We introduce an information-theoretic approach…Juan De Gregorio, David Sánchez, Raúl Toral·Oct 13, 2025SaveLearn
Efficiency correction of particle-averaged quantitiesWe derive analytic formulas to reconstruct particle-averaged quantities from experimental results that suffer from the efficiency loss of particle measurements. These formulas are derived under the…Masakiyo Kitazawa, ShinIchi Esumi, Takafumi Niida et al.·Oct 11, 2025SaveLearn
Optimal Binning for Small-Angle Neutron Scattering Data Using the Freedman-Diaconis RuleSmall-Angle Neutron Scattering (SANS) data analysis often relies on fixed-width binning schemes that overlook variations in signal strength and structural complexity. We introduce a statistically…Jessie E. An, Chi-Huan Tung, Changwoo Do et al.·Oct 10, 2025SaveLearn
Generalizations of Langbein's Formula under Non-Stationarity, Mixed Populations, and Over- or Under-Dispersion in the Number of ExceedancesSince its publication in 1949, Langbein's formula has been applied ubiquitously in both research documents and national guidelines concerning frequency analyses (FAs) of hydrologic extremes. Given a…Francesco Dell'Aira, Antonino Cancelliere, Claudio I. Meier·Sep 28, 2025SaveLearn
Resolving features and derivatives in noisy data using weighted Whittaker-Henderson smoothingA frequently occurring challenge in experimental and numerical observations is how to resolve features, such as spectral peaks - with center, width, height - and derivatives from measured data with…Bert Mulder, Ad Lagendijk, Willem L. Vos·Sep 26, 2025SaveLearn
Particle Identification with MLPs and PINNs Using HADES DataIn experimental nuclear and particle physics, the extraction of high-purity samples of rare events critically depends on the efficiency and accuracy of particle identification (PID). In this work, we…Marvin Kohls·Sep 22, 2025SaveLearn
Comment on Frank Porter, "Confidence intervals for the Poisson distribution"Frank Porter has recently posted a review of "Confidence intervals for the Poisson distribution" (arXiv:2509.02852). The long, diverse history of such intervals is closely related to that of…Robert D. Cousins·Sep 22, 2025SaveLearn
Bertrand's Representation of the Optimal DetectorIt is shown how the optimal detector of Gaussian signals can be represented in terms of Bertrand's class of time-frequency distributions. In this representation, the detector is a correlation between…Vladimir Lenok·Sep 12, 2025SaveLearn
Revisiting the Question of Information Content of EXAFS Spectra through a Bayesian ApproachOver the last several decades the Shannon-Nyquist criterion has been widely used as a measure of the maximum information content in EXAFS spectra and provided an upper limit on the number of…Lucy Haddad, Diego Gianolio, Andrei Sapelkin·Sep 9, 2025SaveLearn