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.
By analyzing a massive dataset of electronic invoices from Ceará, Brazil, this study uses community detection, revealed comparative advantage, and Maximum Entropy Models to demonstrate that inter-city trade networks form cohesive, modular structures that operate near a "critical point" of economic stability.
The paper proposes the Poisson Log-Normal (PoLoN) process, a non-parametric framework that utilizes Gaussian processes to model Poisson log-rates, enabling effective prediction, signal detection, and parameter extraction for discrete count data.
By employing Tensor Programs and singular perturbation theory to analyze the fast-slow dynamics of infinite-width two-layer neural networks, this paper identifies the specific mechanisms and conditions—such as data nonlinearity and initial weight scales—that drive the phenomenon of feature unlearning during stochastic gradient descent.
DerivKit is a Python package that provides stable numerical differentiation tools to bridge the gap between fast Fisher-matrix forecasting and computationally intensive MCMC sampling by enabling higher-order likelihood approximations.
This paper demonstrates that even in high-statistics counting experiments, finite Monte Carlo sample sizes used to model systematic uncertainties cause the standard asymptotic approximations for profile-likelihood ratio confidence intervals to fail, resulting in systematic under-coverage.
Almanac is a Hamiltonian Monte Carlo-based framework that extracts noiseless all-sky maps and their corresponding power spectra from noisy cosmological observations across multiple redshift bins, providing model-independent posterior data products that avoid issues like $EB$-leakage and enable robust diagnostics of systematic errors or new physics.
This study compares five distinct void-finding algorithms applied to the IllustrisTNG simulation to demonstrate that while the radial gradient of individual galaxy bias within cosmic voids is a robust feature, the specific selection of anti-biased galaxies and contamination by high-bias boundary galaxies depend significantly on the adopted void definition and density thresholds.
This paper introduces MoreFit, a high-performance framework for unbinned maximum likelihood parameter estimation in particle physics that leverages just-in-time compiled computation graphs, novel automatic optimizations, and heterogeneous backends (OpenCL and LLVM/Clang) to achieve superior speed and efficiency across diverse hardware platforms.
This paper systematically evaluates the sensitivity of four semi-supervised anomaly detection methods to untunable hyperparameters in the search for Beyond the Standard Model physics and proposes a robust, non-parametric permutation test for statistical assessment.
This paper unifies Gaussian process regression with dynamic mode decomposition to address the scalability and hyperparameter optimization challenges of kernel-based Koopman operator methods, thereby improving computational efficiency and noise resilience in modeling nonlinear dynamical systems.