309 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 presents a comprehensive study of the expected precision for Higgs boson hadronic decay modes () at the FCC-ee, demonstrating that combining $ZH$ and Vector boson fusion processes with four IDEA detectors will achieve percent-to-per-mil level measurements and provide the first sensitivity to evidence the rare decay.
This paper addresses limitations in existing resonant anomaly detection methods by introducing new simulated signal benchmarks and a comprehensive "kitchen sink" observable set combining Energy Flow Polynomials and subjettiness variables, demonstrating that this approach offers superior sensitivity across diverse signal types while an attribute bagging variant significantly reduces training costs with comparable performance.
This paper utilizes Monte Carlo simulations to determine the critical thresholds for site and bond percolation on the Smith Hat aperiodic monotile, reporting values of approximately 0.8227 for site percolation, 0.7982 for bond percolation, and 0.5442 for site percolation on the dual graph.
This paper systematically evaluates the design and training of scientific machine learning emulators for the MAM4 aerosol microphysics module in E3SMv2, demonstrating that effective scaling, convergence monitoring, and moderate network complexity are critical for accurately reproducing aerosol concentration changes under cloud-free conditions.
This study applies Dynamical Network Marker (DNM) theory to Tokyo Stock Exchange order data, demonstrating that treating market participants as interacting elements within a multivariate time series framework enables the detection of early warning signals for large price movements on a daily scale.
This paper introduces the NP-hard CriticalSet problem for identifying contributors whose removal maximally isolates items in bipartite dependency networks, proving the limitations of greedy approaches and proposing the ShapleyCov centrality measure and the efficient MinCov algorithm, which achieves near-optimal performance with linear-time complexity.
This paper argues that the scientific community often conflates mathematical sparsity with interpretability, proposing instead an operational definition for scientific machine learning that prioritizes mechanistic understanding over sparse expressions to better facilitate the discovery of fundamental physical principles.
This paper establishes an exact identity linking the total entropy production rate in overdamped Langevin dynamics to mutual information and a mean flow term, enabling a forward-only characterization of irreversibility and a canonical nonnegative decomposition of subsystem entropy production into self and interaction components.
This paper proposes and evaluates two Bayesian methods, Laplace approximation and Markov Chain Monte Carlo, for quantifying the uncertainty of hybrid spectral unmixing estimators in -ray spectrometry, demonstrating that while both perform well under ideal conditions, Markov Chain Monte Carlo remains robust when spectral constraints or dominant backgrounds create non-Gaussian posterior distributions where Laplace approximation fails.
This paper compares widely used weighted average and median methods for computing a common mean from independent measurements and proposes a new combined estimate to provide more robust and realistic results, particularly when dealing with small samples, biases, or discrepant data.