physics.data-an papers | Gist.Science

This section explores the intersection where physics meets data analysis, a rapidly evolving frontier where complex datasets reveal hidden patterns in the universe. From tracking particle collisions to modeling cosmic structures, these studies rely on advanced statistical methods to turn raw numbers into fundamental insights about how reality works.

Gist.Science monitors every new preprint in this category as it appears on arXiv, ensuring you never miss a breakthrough. We process each entry to provide both plain-language overviews for general understanding and detailed technical summaries for experts, bridging the gap between dense research and clear comprehension.

Below are the latest papers in physics and data analysis, organized for easy reading and discovery.

Data-Driven Automated Identification of Optimal Feature-Representative Images in Infrared Thermography Using Statistical and Morphological Metrics

This paper proposes a data-driven, unsupervised methodology for automatically identifying optimal defect-representative images in infrared thermography by utilizing three complementary statistical and morphological metrics—the Homogeneity Index of Mixture, Representative Elementary Area, and Total Variation Energy—to overcome the limitations of conventional evaluation methods that require prior knowledge of defect locations.

Harutyun Yagdjian, Martin Gurka2026-04-14🔬 physics.app-ph

Blume-Capel model: Estimation of a three stable state network for $-\bf 1$ , $\bf 0$ and $\bf +1$ data

This paper proposes the Blume-Capel model as an extension of the Ising model for analyzing three-state data ( $-1, 0, +1$ ), demonstrating that combining pseudo-likelihood with lasso techniques enables accurate parameter estimation and confidence interval construction for small networks, as validated by applications to voting preference data from Stemwijzer.

Lourens Waldorp, Jonas Dalege, Maarten Marsman, Adam Finnemann, Irene Ferri, Han L. J. van der Maas2026-04-14📊 stat

Vestibular reservoir computing

This paper proposes a physically implementable vestibular-inspired reservoir computing scheme with an uncoupled topology that achieves performance comparable to fully coupled networks by theoretically demonstrating equivalent memory capacity under specific conditions.

Smita Deb, Shirin Panahi, Mulugeta Haile, Ying-Cheng Lai2026-04-14🌀 nlin

Optimal Null-Constrained Source-Basis Sensing in a Time-Reversed Young Interferometer

This paper establishes a general theory for optimal null-constrained sensing in time-reversed Young interferometers, demonstrating that source patterns can be engineered to enforce a vanishing nominal response while preserving finite sensitivity through inverse-noise-weighted projection, thereby quantifying the information loss as a geometric factor dependent on the overlap between nominal and derivative responses.

Jianming Wen2026-04-14🔬 physics.optics

Surface correlation functions of dead-leave models

This paper derives exact analytical expressions for pore-surface and surface-surface correlation functions within the general class of dead-leave models, demonstrating their validity for arbitrary grain shapes and dimensions while highlighting distinct structural differences compared to numerically reconstructed Debye random media.

Cedric J. Gommes2026-04-14🔬 cond-mat.mtrl-sci

Emergence of Complex Structures

This paper proposes a unified framework that resolves the tension between entropy growth and the emergence of complex structures by demonstrating how coarse-grained spatial ordering can coexist with increasing phase-space complexity through a geometric transport approach that links deformation tensors, nonlocal interactions, and Landau--Ginzburg self-organization, with applications extending from cosmological structure formation to broader mesoscopic systems.

Francisco-Shu Kitaura2026-04-14🌀 nlin

A unified descriptor framework for hydrogen storage capacity and equilibrium pressure in interstitial hydrides

This paper establishes a unified, interpretable data-driven framework that decouples the governing mechanisms of hydrogen storage capacity and equilibrium pressure in interstitial hydrides, revealing that capacity is determined by geometric and thermal properties while pressure is controlled by elastic moduli, thereby enabling the rational design of materials with optimized performance.

Seong-Hoon Jang, Di Zhang, Xue Jia, Hung Ba Tran, Linda Zhang, Ryuhei Sato, Yusuke Hashimoto, Yusuke Ohashi, Toyoto Sato, Kiyoe Konno, Shin-ichi Orimo, Hao Li2026-04-14🔬 cond-mat.mtrl-sci

EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules

EnScale is a computationally efficient, generative machine learning framework that utilizes proper scoring rules to produce accurate, multivariate, and temporally consistent high-resolution climate downscaling from coarse global circulation models, effectively emulating regional climate models at a fraction of the cost.

Maybritt Schillinger, Maxim Samarin, Xinwei Shen, Reto Knutti, Nicolai Meinshausen2026-04-13📊 stat

Variational Dimension Lifting for Robust Tracking of Nonlinear Stochastic Dynamics

This paper proposes a variational dimension-lifting framework that constructs an invertible transformation to map nonlinear stochastic state-space models into higher-dimensional linear Gaussian systems, enabling robust tracking via standard linear inference techniques while maintaining accuracy and stability in challenging regimes.

Yonatan L. Ashenafi2026-04-13📊 stat

New Deep Learning Data Analysis Method for PROSPECT using GAPE: Genetic Algorithm Powered Evolution

This paper introduces GAPE, a genetic algorithm-powered evolution method that optimizes deep learning models for the PROSPECT experiment, achieving a nearly 2.8-fold improvement in signal-to-background ratio for reactor antineutrino identification while addressing and mitigating time-dependent training biases.

M. Adriamirado, A. B. Balantekin, C. Bass, O. Benevides Rodrigues, E. P. Bernard, N. S. Bowden, C. D. Bryan, T. Classen, A. J. Conant, N. Craft, A. Delgado, G. Deichert, M. J. Dolinski, A. Erickson, M (…)2026-04-13⚛️ hep-ex

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