🔬 Category

physics.data-an

261 papers

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.

On the use of the Derivative Approximation for Likelihoods for Gravitational Wave Inference

This paper presents a comprehensive comparison of gravitational wave inference methods, demonstrating that the Derivative Approximation for Likelihoods (DALI) offers a significantly more accurate and computationally efficient alternative to traditional MCMC and Fisher Matrix approaches, while introducing the public \texttt{GWDALI} code to facilitate rapid and precise posterior estimation for next-generation observatories.

Josiel Mendonça Soares de Souza, Miguel Quartin2026-04-16⚛️ gr-qc

The High W Challenge: Robust Neutrino Energy Estimators for LArTPCs

This paper introduces and evaluates a new W2^2-based neutrino energy estimator for liquid-argon time-projection chambers, demonstrating that while it offers superior robustness against modeling uncertainties and minimal bias in the transition region between scattering regimes, its slightly worse energy resolution under ideal conditions suggests a complementary role alongside more exclusive methods for future oscillation analyses.

Christopher Thorpe, Elena Gramellini2026-04-16⚛️ hep-ex

Physics-driven Comparative Analysis of Various Statistical Distance Metrics and Normalizing Functions

This paper presents a data-driven comparative analysis of various statistical distance metrics and normalizing functions using electron and photon events from a decaying Kr-83 isotope to evaluate the stability of a dimensionless Parameter of Interest under different conditions.

Nafis Fuad (Center for Exploration of Energy,Matter, Indiana University, Bloomington, IN 47405, USA)2026-04-16⚛️ nucl-ex

Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions

This paper demonstrates that the OmniLearned particle physics foundation model, pre-trained on high-energy collision data, can be effectively transferred to low-energy fixed-target neutrino experiments to outperform models trained from scratch on tasks like energy regression and pion classification, thereby validating the potential for detector-agnostic inference across vastly different energy scales and physics processes.

Gregor Krzmanc, Vinicius Mikuni, Benjamin Nachman, Callum Wilkinson2026-04-15⚛️ hep-ex

Hierarchical Maximum Likelihood Estimation for Time-Resolved NMR Data

This paper proposes a hierarchical maximum likelihood estimation method based on a Bayesian model that improves the accuracy and uncertainty propagation of time-resolved NMR data analysis for metabolic monitoring, outperforming traditional two-stage procedures and Fourier methods in both high-field and micronscale experimental setups.

Lennart H. Bosch, Pernille R. Jensen, Nico Striegler, Thomas Unden, Jochen Scharpf, Usman Qureshi, Philipp Neumann, Martin Gierse, John W. Blanchard, Stephan Knecht, Jochen Scheuer, Ilai Schwartz, Mar (…)2026-04-14🧬 q-bio

Graph-based Summary Statistics for Revealing the Stochastic Gravitational Wave Background in Pulsar Timing Arrays

This paper proposes a graph-based method using pulsar timing residuals to detect the stochastic gravitational wave background by analyzing network structural characteristics, demonstrating its ability to identify signals with a strain amplitude of ≳1.2×10−15\gtrsim 1.2\times 10^{-15} and providing weak evidence for an SGWB in the NANOGrav 15-year dataset.

M. Alakhras, S. M. S. Movahed2026-04-14🔭 astro-ph

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