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 introduces BayesMBAR, a Bayesian generalization of the multistate Bennett acceptance ratio (MBAR) method that computes free energy posterior distributions to provide more accurate uncertainty estimates and allow for the incorporation of prior knowledge, such as surface smoothness, into free energy calculations.
This paper challenges the common assumption that divisible statistics like Pearson's serve as effective goodness-of-fit tests in sparse data regimes with many bins, proposing instead a unifying framework that reveals the limitations of existing methods and offers modified, more powerful alternatives along with new distribution-free tests.
This paper utilizes Random Matrix Theory to investigate spectral fluctuations in multilayer networks, demonstrating that universal statistical features persist across varying connectivity configurations and successfully modeling the crossover between independent and fully coupled layer statistics, with applications validated on real protein structures.
This paper introduces a variational framework that represents directed interactions as edge flows on a simplicial complex to extract a low-dimensional Hilbert space of persistent harmonic cycles, enabling scalable statistical inference and revealing reproducible large-scale recurrent organization in high-dimensional systems like human fMRI data that traditional pairwise methods miss.
This paper introduces the Reweighting Adversarial Network (RAN), a novel unbinned unfolding technique that utilizes a particle-level reweighting function guided by a Wasserstein critic to overcome support overlap limitations and outperform state-of-the-art methods in accuracy and computational efficiency.
The paper introduces **PoissonRatioUQ**, an R package designed for Bayesian modeling and uncertainty quantification of count ratios by focusing on the ratio of Poisson means, offering flexible options for both spatial and non-spatial data as well as specialized handling for intensity ratio transformations.
This paper demonstrates that job-related Google search data significantly improves the accuracy of nowcasting and forecasting unemployment rates across the Visegrad Group countries (Czech Republic, Hungary, Poland, and Slovakia).
This study applies hierarchical clustering to corruption perception data from 134 countries, revealing a distinct global hierarchy where corruption levels strongly correlate with economic development and GDP per capita rankings.
This paper analyzes 623 bolide events from 2007 to 2025 to demonstrate that infrasound detectability is primarily governed by entry geometry, specifically favoring steeper angles and lower-altitude energy deposition, while atmospheric propagation and energy levels act as secondary modulating factors.
This paper proposes and validates a practical methodology to eliminate biases in global polarization measurements caused by asymmetric detector acceptance in fixed-target heavy-ion collision experiments, thereby enabling more accurate studies of spin dynamics across the QCD phase diagram.