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
Analytically Consistent Reconstruction of Finite Data Using Padé SequencesReconstructing the analytic structure of a function from finite datasets is a fundamental problem across theoretical, numerical, and experimental physics. While Padé approximants provide a natural…Emerson Díaz, Balma Duch, Pere Masjuan·Aug 9, 2026SaveLearn
Uncertainties in ROC (Receiver Operating Characteristic) Curves Derived from Counting DataThe ROC (receiver operating characteristic) curve is a widely used device for assessing decision-making systems. It seems surprising, in view of its history dating back to World War Two, that the…M. P. Fewell·Aug 7, 2026SaveLearn
Weak Form Recovery of Heston Type Stochastic DynamicsEstimating the coupled drift, diffusion, and leverage structure of a stochastic-volatility model directly from a price path is an unresolved inverse problem: Kramers--Moyal increment estimators…Sai Sathvik Gullipalli, Eshwar R A, Gajanan V. Honnavar·Aug 6, 2026SaveLearn
Learned proposals in trans-dimensional inference are optimal at equilibrium, not during assemblyInferring the dimension of a model - the number of components needed to explain data - jointly with the parameters is a pervasive problem, from counting sources in an image to mixture modeling, and…Argyro Sasli, Nikolaos Karnesis, Minas Karamanis et al.·Aug 4, 2026SaveLearn
Lossless Compression Performance for PETRA III DatasetsLarge-scale research facilities increasingly face the challenge of managing rapidly growing data volumes while maintaining sustainable archival infrastructures. We present the first comprehensive…Malte Buschmann, Yannis Schumann, Christian Voss et al.·Jul 31, 2026SaveLearn
Discovery Sensitivity for a Counting Experiment with Background UncertaintyIn Particle Physics, a search for a new signal process is often based on observing a Poisson-distributed number of events, whose mean contains contributions from background and, if it exists, the…Enzo Canonero, Glen Cowan·Jul 31, 2026SaveLearn
Learning transferable event representations for charmed baryon physics at BESIIIDeep learning has become an essential tool in high-energy physics, where the ability to learn transferable event representations can significantly improve model generalization across related physics…Kaixuan Huang, Yangu Li, Junpeng Zhao et al.·Jul 31, 2026SaveLearn
Rethinking Total Absorption Gamma Spectroscopy Deconvolution: Supervised Machine Learning vs Response-Matrix MethodsThe extraction of β-feeding distributions in Total Absorption γ-ray Spectroscopy constitutes a challenging inverse problem, particularly in nuclei with complex decay schemes involving a large…J. Balibrea-Correa, E. N{á}cher, C. Fonseca-Vargas et al.·Jul 30, 2026SaveLearn
Unveiling Amplitude Distributions via the Ordinal Language of Random WalksOrdinal patterns are widely used to characterize temporal organization in time series, yet they are often considered insensitive to the amplitude distribution of the data. In this work, we show that…Alberto Mateos Roig, Luciano Zunino, Felipe Olivares·Jul 30, 2026SaveLearn
Inverse generalised spin models of answers to questionnairesNetwork psychometrics conceptualises psychological constructs as emergent properties of systems of interacting items. Energy-based probabilistic models have gained popularity as models of these…Arianna Armanetti, Luca Cecchetti, Paolo Sarti et al.·Jul 30, 2026SaveLearn
A Two-Regime Statistical Framework for Wind-Power Distributions: From Wind-Speed Fluctuations to Turbine ControlWind-power variability is a major challenge for the reliable integration of utility-scale wind energy into modern power systems. Although wind-speed statistics are often described by simple…S. Mitra, S. E. Lakhal, C. P. Connaughton et al.·Jul 28, 2026SaveLearn
Rician Distribution as a Physically Interpretable Model for Wind-Speed StatisticsThe statistics of atmospheric wind variations are commonly modeled using Gaussian or Weibull forms, which often trade physical interpretability against statistical accuracy, especially in the…S. Mitra, S. E. Lakhal, C. P. Connaughton et al.·Jul 28, 2026SaveLearn
Visibility graph-based characterization of extreme values in time seriesComplex dynamical systems often display extreme fluctuations of an observed variable that constitute significant deviations from the long-term average, and which are often associated with severe…Juliane T. Moraes, Lucas Lacasa, Cristina Masoller·Jul 28, 2026SaveLearn
Data Field Theory: Theory and Applications of the Functional Renormalization Group for Signal DetectionWe review the renormalization group framework for signal detection in high-dimensional data, tailored to the regime where the signal may be of extensive rank and does not separate from the noise bulk…Riccardo Finotello, Vincent Lahoche, Dine Ousmane Samary et al.·Jul 24, 2026SaveLearn
A framework for general discrete probability calculationsProbability follows a simple and concise set of rules. In practice, however, reason- ing about probability may be highly unintuitive and this leads to the possibility of miscalculations even for…Kacper Topolnicki, Roman Skibiński·Jul 23, 2026SaveLearn
Accelerating Electrochemical Impedance Spectroscopy Measurements by Reducing Reliance on Noisy Low-Frequency DataElectrochemical impedance spectroscopy (EIS) is a powerful tool for probing kinetic and transport processes in electrochemical systems, but its practical use is often limited by the long acquisition…Qiuyu Shi, Naohiro Fujinuma, Yonatan Kurniawan et al.·Jul 21, 2026SaveLearn
Inferring Non-Normal Amplification Geometry from Multivariate Time SeriesAcross hydrodynamics, ecology, neuroscience, network dynamics, non-Hermitian physics, and socio-economic systems, asymptotically stable dynamics can exhibit large transient amplifications that are…V. R. Saiprasad, V. Troude, D. Sornette·Jul 16, 2026SaveLearn
Profile-Likelihood and Baseline-Sensitivity Diagnostics for Digitized Radiation-Sensor Decay DatasetsAccurate interpretation of radiation-sensor decay data is important for environmental monitoring, site remediation, radiation metrology, detector quality assurance, and nuclear data evaluation. When…Victor V. Golovko·Jul 14, 2026SaveLearn
Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based ExplorationEarth system science is producing increasingly large, high-dimensional datasets from both physics-based and AI-driven models. While embedding-based representations make these data searchable and…Nihanth W. Cherukuru, Matt Rehme, Kirsten J. Mayer et al.·Jul 13, 2026SaveLearn
Quantification of Electron Energy-Loss SpectraThis manuscript summarizes the recent developments in EELS quantification flow as will be implemented in the CEOS Panta Rhei and TEMDM software. This should serve as a technical reference for the…Pavel Potapov, Giulio Guzzinati·Jul 12, 2026SaveLearn