316 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 summarizes recent advancements in using constituent-based machine learning architectures, such as graph neural networks and transformers, to classify hadronic final states in the ATLAS Experiment, detailing their performance on simulated and real data while outlining future directions for data-driven and model-independent tagging strategies.
This paper introduces Randomly Modulated Gaussian Processes as a unifying framework that generalizes diverse anomalous diffusion models in heterogeneous media, enabling systematic statistical characterization, deriving key metrics for experimental classification, and facilitating biophysical interpretations of single-particle trajectories.
This paper reviews recent advancements in pattern recognition and data analysis for Ring Imaging Cherenkov (RICH) detectors, covering improvements in traditional reconstruction methods, the integration of modern machine learning, and emerging trends like global particle identification and generative models for simulation.
This study presents globally inverted pressure-temperature phase diagrams for Fe, MgO, SiO2, and MgSiO3 up to 5,000 GPa, derived from machine learning and experimental data, which resolve long-standing disputes regarding their melting curves and refine internal structure models for giant and super-Earth exoplanets.
This paper proposes a method to inflate uncertainties in Bayesian priors to ensure conservative posterior results when combining experiments with unknown correlations between their nuisance parameters.
This paper introduces a robust information-theoretic framework based on predictability gain to quantify temporal memory in discrete stochastic processes, demonstrating its superiority over traditional criteria and revealing that daily precipitation in the contiguous United States is well-described by low-order Markov chains with distinct regional and seasonal variations.
This paper introduces Ion Count-Aided Microscopy (ICAM), a quantitative imaging technique that statistically estimates secondary electron yield to substantially reduce shot noise and enable high-quality, low-dose imaging of fragile nanoscale samples.
This paper introduces a novel methodology for analyzing neutron and x-ray scattering event data that bypasses traditional histogramming and least-squares fitting to achieve significantly higher accuracy and efficiency while reducing systematic errors, despite requiring increased computation time and offering a less intuitive approach.
This paper introduces a maximum-entropy framework for continuous-time temporal networks that factorizes interactions into global time processes and static edge probabilities, enabling closed-form analytical solutions and improved generative modeling via non-homogeneous Poisson processes.
This paper presents algorithms for estimating Detector Error Models (DEMs) directly from syndrome data without decoders, applying them to Google's Willow chips to reveal that while DEMs optimized for syndrome likelihood better predict unseen data, those optimized for logical performance serve as superior decoder priors, while also uncovering long-range correlated measurement errors and unmodeled artifacts like radiation events.