Eigenvector rotation precedes eigenvalue-based early-warning signals: a TVP-Kalman approach to detecting critical transitionsEarly-warning signals (EWS) for critical transitions are predominantly based on changes in the dominant eigenvalue of the system's Jacobian-rising variance and lag-1 autocorrelation (AR(1)).…Gildas Tiwang Ngueuleweu·Jul 11, 2026SaveLearn
Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics BreakthroughMachine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing. As ML systems grow increasingly…Gaia Grosso, Vinicius Mikuni, Lukas Heinrich·Jul 10, 2026SaveLearn
Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networksReconstructing charged-particle tracks in silicon detectors is a central task in high-energy physics experiments and a key component of both offline reconstruction and online event selection. Within…Carlo Varni, Krzysztof Ciesla, Marcin Wolter et al.·Jul 7, 2026SaveLearn
Jet flavor tagging with Particle Transformer for Higgs factoriesWe study the performance of the Particle Transformer (ParT) for jet flavor tagging using ILD full simulation events (1M jets) as well as fast simulation samples (10M and 1M jets). We perform…Taikan Suehara, Takahiro Kawahara, Tomohiko Tanabe et al.·Jul 1, 2026SaveLearn
The Squealer: Sensification of model exploration and model misfitWe introduce a method for visual and auditory feedback when exploring the fit of a model to data. Starting with a best-fit curve fit to data, the user can drag the curve to a new position and the…Andrew Gelman, Andrew H. Jaffe, Eliot Carlson et al.·Jun 29, 2026SaveLearn
Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in PhysicsWe review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning the structural transparency of a model (the…Rikab Gambhir, Luisa Lucie-Smith, Jesse Thaler·Jun 24, 2026SaveLearn
Where Is My Physics Wrong? Localized and Identifiable Discovery of Model DiscrepancyHybrid models combine trusted physics with data-driven correction, but a physical model is rarely wrong everywhere or in the same way. The key diagnostic question is local: where does the model fail,…Yifan Wang·Jun 22, 2026SaveLearn
Optimal and Adaptive Bayesian Sampling for Non-Linear Parameter Estimation under White NoiseThe question of optimal experimental design has been addressed in a vast variety of contexts and answered using manifold approaches. Assuming additive white Gaussian noise, this work applies the…Lennart H. Bosch, Martin B. Plenio·Jun 18, 2026SaveLearn
A Numerical Rosenblatt Method for Forced Variable IndependenceA novel numerical technique is presented to transform one random variable within a system toward statistical quasi-independence from any other random variable in the system. The method's…Radek Vavřička, Tomáš Sýkora·Jun 13, 2026SaveLearn
Peak-Based Nuclide Identification in HPGe γ-Spectrometry with Machine Learning and SHAPHigh-purity germanium gamma spectra often require time-consuming analyses from subject matter experts. Photopeaks within these spectra are carefully fitted and numerical methods are employed to…Samuel Emmons, Kelly Truax, Maurice Lonsway et al.·Jun 12, 2026SaveLearn
A Unified Framework for Structured Flow Modeling: From Representation to Verification and Model DiscoveryMany dynamical systems can be described in terms of structured flows combining source/sink behavior, cyclic dynamics, and topology-constrained transport. These features arise across a wide range of…Diego Casadei·Jun 12, 2026SaveLearn
Interpretable model-free inference of parametric variation across time-series data through large-scale feature extractionHere we address the problem of estimating the dimensionality and nature of parametric variation in an unknown generative process directly from time-series data, without specifying or fitting a model.…Ben D. Fulcher, Carl H. Lubba, Giorgio F. Gilestro et al.·Jun 11, 2026SaveLearn
Information bottleneck for learning the phase space of dynamics from high-dimensional experimental dataIdentifying the dynamical state variables of a system from high-dimensional observations is a central problem across physical sciences. The challenge is that the state variables are not directly…K. Michael Martini, Eslam Abdelaleem, Paarth Gulati et al.·Jun 10, 2026SaveLearn
Accurate Estimation of Mutual Information in High Dimensional DataMutual information (MI) quantifies statistical dependence between variables and is widely used across scientific disciplines, yet accurate estimation from finite data remains notoriously difficult.…Eslam Abdelaleem, K. Michael Martini, Ilya Nemenman·Jun 10, 2026SaveLearn
Confidence, Statistical Evidence and Relative Belief with Applications to a Problem in Particle PhysicsProbability theory provides a clear definition of what is meant by evidence in favor, against or none either way, of an event occurring for an unobserved response, via the principle of evidence. This…Michael Evans, Siqi Zheng·Jun 8, 2026SaveLearn
Design Principles for AI-Ready QCD Data with a Barrel Imaging Calorimeter ApplicationData from large physics collider experiments in Quantum Chromodynamics (QCD) research differ fundamentally from the modalities used in modern foundation models. The heterogeneity of detector readouts…Zhiwan Xu, Sylvester Joosten, Minho Kim et al.·Jun 4, 2026SaveLearn
The BRUIT-FM "open data" noise reduction challengeThe BRUIT-FM Challenge asks participants to reduce ''noise'' on an open dataset of real and synthetic broadband seafloor seismology data. The dataset includes signals from…Wayne C Crawford, Stephan Ker, Simon Rebeyrol et al.·Jun 4, 2026SaveLearn
A practical methodology for Λ global polarization extraction in fixed-target experimentsNon-central heavy-ion collisions generate large orbital angular momentum in the created medium, which leads to polarization of final-state particles via spin-orbit coupling, known as global spin…Tan Lu, Chengdong Han, Chenlu Hu et al.·Jun 3, 2026SaveLearn
The High W Challenge: Robust Neutrino Energy Estimators for LArTPCsAccurate determination of the neutrino energy is central to precision oscillation measurements. In this work, we introduce the W2-based estimator, a new neutrino energy estimator based on the…Christopher Thorpe, Elena Gramellini·Jun 2, 2026SaveLearn
Higher-order spacings in the superposed spectra of random matrices with comparison to spacing ratios and application to complex systemsHigher-order spacing statistics in the m superposed spectra of circular random matrices of the same class are studied numerically. We conjecture that for given m (or order k) and β, the…Sashmita Rout, Udaysinh T. Bhosale·Jun 2, 2026SaveLearn
Proton High-Order Cumulants in Au+Au Collisions at High Baryon Density from JAM with a Centrality-Independent FrameworkThe event-by-event higher-order cumulants of conserved quantities such as net-baryon, net-electric charge, and net-strangeness in heavy-ion collisions have been extensively utilized in experimental…Yongcong Xu, Zhaohui Wang, Yu Zhang et al.·May 30, 2026SaveLearn
Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and PracticeSearches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of possible signals.…Oz Amram, Marco Letizia, Mikael Kuusela·May 29, 2026SaveLearn
FitED: A User-Centric, Extensible Software Environment for Robust Peak-Profile and General Functional Data FittingReliable parameter extraction from experimental data is essential for quantitative analysis across spectroscopy, diffraction, photoluminescence, chromatography, microscopy, and time-resolved…Mustafa Mahmoud Aboulsaad·May 29, 2026SaveLearn
On the Statistical Interpretation of Discoveries in LHC DataWe examine discovery criteria at the Large Hadron Collider (LHC) within a model-independent framework, with particular emphasis on the statistical signatures of new physics. This study is motivated…S. V. Chekanov, E. J. Weik·May 23, 2026SaveLearn
Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak EstimationSpectral deconvolution is essential for extracting peak structures that encode material properties and chemical structures, but conventional automated methods often fail when spectra contain…Hayato Okubo, Yoshifumi Amamoto, Toshimitsu Aritake et al.·May 17, 2026SaveLearn