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.
This paper reveals a largely system-independent power-law relationship between the coupling strength required to trigger extreme events in networked dynamical systems and the topological or spectral properties of their coupling structures.
This paper introduces the Topological Stability Index (TSI), a variance-based scalar measure for persistence barcodes that quantifies absolute lifetime dispersion and complements entropy-based summaries by capturing structural variability in stochastic fluctuations while remaining insensitive to deterministic trends.
This paper introduces a deep learning approach incorporating surrogate information gain (SIG) for optimal data selection in nuclear spin detection, achieving significant reductions in experimental time (up to 85%) while maintaining high precision and robustness against imperfections in both high-field and low-field regimes.
This paper proposes an information-theoretic method for inferring signed brain networks from multivariate time series by comparing empirical data against entropy-constrained benchmarks, revealing that the brain exhibits structural frustration primarily driven by subcortical and limbic regions, with modular organization aligning with the statistical variant of Relaxed Balance Theory.
This paper introduces a deterministic "Inefficiency Metric" that leverages Principal Component Analysis on six years of Hedera transaction data to quantify structural stress in decentralized networks by linking topological fluctuations, such as effective diameter and closeness centrality, to macroeconomic events and ecosystem dynamics.
This paper introduces a novel spectral density derivation for correlated random matrices that unifies the Marchenko-Pastur and elliptic laws, revealing that hetero-associative memory networks (equivalent to linear attention) exhibit non-monotonic stability dependent on the number of memorized patterns.
This study develops a more accurate and numerically stable approximation of the Universal Thermal Climate Index (UTCI) by employing sparse regression with orthogonal Legendre polynomials, which significantly reduces both average and large errors compared to the standard 6th-degree polynomial method while maintaining computational efficiency.
This paper clarifies the assumptions underlying the standard Poisson-based calibration method for high-resolution cryogenic detectors using monoenergetic photons, analyzes how realistic detector performance violates these assumptions to introduce bias, and evaluates the specific impact of detector parameters on calibration accuracy.
This paper presents an AI-driven, label-free Surface-enhanced Raman spectroscopy (SERS) platform that enables rapid, high-accuracy identification of extracellular vesicles from diverse cellular origins in non-invasive tear and sweat samples, offering a promising tool for personalized disease diagnosis.
This paper derives closed-form expressions for target state moments in log-polar coordinates to develop a computationally efficient CFE-UKF that avoids sigma point propagation while leveraging higher-order statistics to manage non-Gaussianity and control range estimation errors during ownship maneuvers.