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.

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

This paper introduces Stochastic Particle Advection Velocimetry (SPAV), a novel framework utilizing a statistical data loss and physics-informed neural networks to significantly improve the accuracy of particle tracking velocimetry by explicitly modeling particle advection and accounting for localization uncertainties, thereby reducing reconstruction errors by approximately 50% in both simulated and experimental fluid flow measurements.

Ke Zhou, Jiaqi Li, Jiarong Hong, Samuel J. Grauer2026-03-31🔬 physics

Neural optical flow for planar and stereo PIV

This paper introduces Neural Optical Flow (NOF), a continuous neural-implicit framework that enhances the accuracy, robustness, and data compression of particle image velocimetry (PIV) by integrating differentiable image warping, physical constraints like Navier-Stokes residuals, and tailored network expressivity to enable advanced analysis of both planar and stereo flows.

Andrew I. Masker, Ke Zhou, Joseph P. Molnar, Samuel J. Grauer2026-03-31🔬 physics

Chiral moments make chiral measures

This paper introduces a robust, flexible framework for quantifying the chirality and handedness of distributions by constructing rotationally-invariant pseudoscalars from tensorial moments that capture both angular and radial dependencies, validated through toy models and photoionization applications alongside an accompanying open-source software package.

Emilio Pisanty, Nicola Mayer, Andrés Ordóñez, Alexander Löhr, Margarita Khokhlova2026-03-31🔬 physics

Deep brain microelectrode signal: $q$ -statistical approach

This study characterizes deep brain microelectrode signals from Parkinson's disease patients using a $q$ -statistical approach, revealing that while the amplitude distributions follow a $q$ -Gaussian form indicative of long-range correlations, the tight functional coupling between the $q$ and $\beta$ parameters across recordings serves as the specific signature of near-critical dynamics in the parkinsonian brain, rather than the $q > 1$ value itself.

Ana Luiza Souza Tavares, Henrique Santos Lima, Artur Pedro Martins Neto, Bruno Duarte Gomes, Constantino Tsallis2026-03-31🔬 physics

Solving the inverse problem of X-ray absorption spectroscopy via physics-informed deep learning

This paper introduces the Spectral Pattern Translator (SPT), a physics-informed deep learning framework that leverages Fourier duality to robustly invert X-ray absorption spectra into transient atomic configurations, thereby overcoming the simulation-to-experiment gap and enabling millisecond-scale autonomous materials discovery.

Suyang Zhong, Boying Huang, Pengwei Xu, Fanjie Xu, Yuhao Zhao, Jun Cheng, Fujie Tang, Weinan E, Zhong-Qun Tian2026-03-31🔬 cond-mat.mtrl-sci

Compressing Transformer Language Models via Matrix Product Operator Decomposition: A Case Study on PicoGPT

This paper demonstrates that Matrix Product Operator (MPO) decomposition is a practical and theoretically grounded method for compressing Transformer language models, achieving up to 13x parameter reduction in PicoGPT while retaining over 97% of the baseline token accuracy.

Younes Javanmard, Tanmoy Pandit, Masoud Mardani2026-03-31💬 cs.CL

Identifying preferred routes of sharing information on social networks

This study demonstrates that information dissemination on social networks is not random but follows specific, discernible patterns through global and local preferential selection models, where new content tends to propagate along the same pathways as previous news.

Rozhin Mohammadikian, Parsa Bigdeli, Behrouz Askari, G. Reza Jafari2026-03-30🔬 physics

Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer

This paper presents two machine-learning approaches for the ATLAS Muon Spectrometer to address High-Luminosity LHC challenges: a Graph Neural Network that improves background-hit rejection and reconstruction speed by 15%, and a Vision Transformer proof-of-concept achieving 98% tracking efficiency in just 2.3 ms.

Jonathan Renusch (on behalf of the ATLAS Collaboration)2026-03-30⚛️ hep-ex

Bayesian estimation of optical constants using mixtures of Gaussian process experts

This paper proposes a Bayesian framework using mixtures of Gaussian process experts to flexibly model absorption spectra, statistically integrate Kramers-Kronig relations with error-aware anchoring, and automatically select measurement points for robustly estimating the complex refractive index of materials like gallium arsenide, potassium chloride, and transparent wood.

Teemu Härkönen, Hui Chen, Erik Vartiainen2026-03-30📊 stat

Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector

The paper introduces "AutoDQM," an automated data quality monitoring system for the CMS detector that utilizes unsupervised machine learning and statistical techniques to identify anomalous data at a rate 4 to 6 times higher than that of good data, thereby enhancing the rapid assessment of detector performance.

Andrew Brinkerhoff, Chosila Sutantawibul, Robert White, Caio Daumann, Chad Freer, Indara Suarez, Samuel May, Vivan Nguyen, Jonathan Guiang, Bennett Marsh, Darin Acosta, Alex Aubuchon, Emanuela Barberi (…)2026-03-27⚛️ hep-ex

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