Data geometry preserves prediction but reshapes explanationAcross scientific domains, empirical data often give rise to positive semidefinite matrices that encode similarities, couplings or interactions and induce natural geometries. We investigate whether…Nicola Amoroso, Mario Caruso, Marianna La Rocca et al.·Oct 6, 2026SaveLearn
Developing Multi-Dimensional, Sequential sWeighting Routines for Reaction Selection with Emphasis on Kaon Identification at CLAS12In nuclear and particle physics experiments, event selection is often a multidimensional classification problem which involves several correlated observables. The widely used sWeight technique…A. Acar, M. Bashkanov, D. P. Watts et al.·Sep 30, 2026SaveLearn
Attributing extreme-event probability to a source variableInformation-flow theory quantifies directional coupling through the rate of change of a target's Shannon entropy, a bulk functional insensitive to the tail of the distribution. We ask instead how…Daniel F. T. Hagan·Sep 29, 2026SaveLearn
Simulation-Based Inference and Unbinned Asimov Construction with Hybrid Neural Density EstimationHigh-dimensional, unbinned neural simulation-based inference often relies on neural ratio estimation, which uses expressive supervised models to estimate density ratios, but a learned ratio by itself…Rafael Coelho Lopes de Sa, Jay Sandesara·Sep 24, 2026SaveLearn
β-Oslo and Shape analyses with unresolved parent-state mixturesAn unresolved mixture of beta-decaying ground and isomeric states populates a common daughter nucleus through different excitation-energy and spin-parity distributions. A framework is developed to…Yangping Shen·Sep 20, 2026SaveLearn
Constrained surrogates for arbitrary families of continuous probability distributionsEmpirical regularities described by parametrized families of probability distributions can shape how complex systems are interpreted and explained. A recurring problem is to determine the extent to…Jack Murdoch Moore, Eduardo G Altmann·Sep 18, 2026SaveLearn
Online local learning for generative thermodynamic computingGenerative thermodynamic computers turn thermal noise into structured data through Langevin dynamics. We train these systems with a local update at each integration step. The reverse-path…Huilin Wang, Weibing Deng·Sep 14, 2026SaveLearn
Information-Loss Location Estimation and Curvature-Scale Analysis of Physical Measurements: A Gaussian, Cauchy, and Logistic Neutron-Lifetime BenchmarkWe develop and implement a two-step location-scale analysis for repeated physical measurements when the underlying distribution is not known. For a chosen substitute distribution, stationarity of…Victor V. Golovko·Sep 13, 2026SaveLearn
Finite Asimov Sample Construction in Unbinned Neural Simulation-Based InferenceExpected sensitivity calculations in frequentist analysis using neural simulation-based inference often require large simulated samples to approximate an unbinned Asimov dataset. We construct a…Rafael Coelho Lopes de Sa, Jay Sandesara·Sep 12, 2026SaveLearn
Combinations of measurements that are simultaneous fits of parameters of interest and systematic uncertainties using the BLUE methodCombining estimates of the same physics parameter obtained from different measurements improves the precision and robustness of the parameter determination. Modern particle physics measurements are…Tomas Dado, Mark Owen, Michele Pinamonti·Sep 10, 2026SaveLearn
A State-Transition Information Space for Time-Series Dynamics: Theory and ApplicationCharacterizing dynamical organization in time series requires distinguishing the diversity of accessible states from uncertainty in their temporal transitions. Here we introduce a state-transition…Dragutin T Mihailović, Vijay P. Singh, Slavica Malinović-Milićević·Sep 8, 2026SaveLearn
Quantity, quality, and timing: Guiding glacier data assimilation strategies in the high ArcticAccurate simulation of glacier surface mass balance is essential for predicting sea level rise and freshwater resources, but it is constrained by uncertainties in meteorological forcing and model…Wenxue Cao, Kristoffer Aalstad, Louise S. Schmidt et al.·Sep 8, 2026SaveLearn
Continuous Event Weighting for Resonance SearchesWe develop a continuous event weighting framework for resonance searches that uses auxiliary event information without dividing the data into increasingly fine sensitivity categories. A hard…Yi Yang, Jun-Jie Chang, Hao-Yin Liao·Sep 4, 2026SaveLearn
Theoretical--operational modelling of complex experiments: parameter robustness and degeneracy in muon--electron conversionComplex experiments infer theory parameters through a coupled chain of physical models and data reduction. We formulate the theoretical--operational model (TOM) as a typed factorization of this…Vitaly Pronskikh·Sep 3, 2026SaveLearn
Reduced latent leakage does not reliably predict lower likelihood bias in collider inferenceReusable collider representations can be evaluated through downstream discrimination and probes of retained information, but neither quantity directly tests the behaviour of score templates in a…Tong Pan·Sep 3, 2026SaveLearn
The Greedy Bump Bias: Local Profiling Geometry and the Look-Elsewhere EffectWhen fitting a localized signal whose position or shape is not known in advance, one typically allows these parameters to vary together with the signal amplitude and chooses the values that maximize…Tommaso Dorigo·Sep 3, 2026SaveLearn
Multi-fidelity Monte Carlo estimation of floor response spectra under combined seismic and structural parameter uncertaintiesFloor response spectra (FRS) are essential tools for the design of non-structural elements (such as equipment or components). Given the various physical phenomena influencing FRS, high-fidelity (HF)…Nils Baillie, Baptiste Kerleguer, Cyril Feau et al.·Sep 1, 2026SaveLearn
Parameter inference from a non-stationary unknown process using statistical feature-based slow feature analysisNon-stationary phenomena are ubiquitous, with examples to be found in climatological measurements, brain activity, and the behavior of financial markets. Starting with a time series from a…Kieran S. Owens, Masako Tamaki, Ben D. Fulcher·Aug 31, 2026SaveLearn
A Probability Model for Pentagonal Prism Dice RollsIn the realm of probability theory and game design, the study of dice probabilities plays a crucial role. The featured dice are usually fair, so their probabilities can be predicted without rolling…Paul R. Hurst, J. Naleo Hyde·Aug 28, 2026SaveLearn
Geometry-native machine learning reconstruction of DSMC moment fields with support monitoringDirect simulation Monte Carlo (DSMC) resolves rarefied-gas dynamics without a constitutive closure, but finite-sample estimates of macroscopic moments converge at markedly different rates. We develop…Ehsan Roohi·Aug 26, 2026SaveLearn
Multivariate amplitude analysis of the cascade particle decays based on the Nearest Neighbors fittingThe Nearest Neighbors estimation of likelihood is implemented for the multivariate amplitude analysis of the cascades of particle decays. In this approach Monte Carlo simulated events that are used…I. V. Yeletskikh, A. O. Vasyukov·Aug 18, 2026SaveLearn
The geometry of uncertainty decomposition in profile-likelihood fitsUncertainty decompositions in profile-likelihood fits are commonly reported through nuisance-parameter impacts, although shifting a fitted parameter and fluctuating the observation that constrains it…Rafael Coelho Lopes de Sá·Aug 15, 2026SaveLearn
Statistical validation of calorimeter inpainting with generative diffusion priorsLocalized detector inefficiencies produce incomplete calorimeter data that limit the ability to perform precision measurements. We address this problem in relativistic heavy-ion collisions from a…Himanshu Raj, Roli Esha·Aug 14, 2026SaveLearn
Unknown Unknowns: Model Misspecification in Machine Learning for PhysicsMachine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed on real data, raising the question not just of…Juan Cruz-Martinez, Carolina Cuesta-Lazaro, Alexander Held et al.·Aug 13, 2026SaveLearn
Exploring new directions in enhancing the ACTS parameter optimization suiteTrack seeding strongly affects both the quality and computational cost of charged-particle reconstruction, yet its many configuration parameters are commonly tuned through expert intuition and…Chance LaVoie, Qi Bin Lei, Rocky Bala Garg et al.·Aug 11, 2026SaveLearn