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 addresses the two-year gap in the pole coordinate of the IERS C01 polar motion series (1858.9–1860.9) by comparing a parametric astronomical model with a data-driven Singular Spectrum Analysis (SSA) approach, ultimately recommending the SSA method as preferable due to its more complete modeling of polar motion.
This paper addresses confusion among physicists regarding Poisson sampling results by comparing various techniques and recommending Garwood's confidence intervals as the most consistent and intuitive method for summarizing data.
This review paper examines the challenges, key advances, and open problems in extending the renormalization group framework from traditional physical systems to the heterogeneous and geometrically complex realm of real-world networks.
This paper systematically evaluates over 6,000 time-series statistics across 35 diverse systems to identify effective data-driven methods for detecting temporal asymmetry, revealing that while no single metric universally captures all forms of irreversibility, specific families of statistics can successfully distinguish irreversible dynamics when tailored to the system's characteristics.
This paper presents the first comprehensive evaluation and ranking of 13 explainable AI (XAI) methods across 24 metrics for head and neck cancer outcome prediction, demonstrating that Integrated Gradients and DeepLIFT consistently achieve high performance in faithfulness, complexity, and plausibility on the HECKTOR dataset.
This paper introduces a rigorous Bayesian framework for MINFLUX microscopy that optimizes scanning patterns to achieve maximal resolution with minimal photons or exposure, potentially reducing the photon count required for 1 nm resolution by a factor of four compared to current heuristic methods.
This paper establishes a reduced optimal-control bound for dynamic soaring that serves as a common mechanical benchmark, revealing that wandering albatrosses fly near this theoretical limit while shearwaters and oystercatchers occupy systematically higher-effort or non-soaring regimes.
This paper presents a proof-of-concept system that utilizes open-weight large language models to extract analysis procedures from high-energy physics publications and generate executable code for reproducing results, demonstrating promising potential as human-in-the-loop tools while highlighting current limitations such as hallucination and execution failures.
This paper introduces NOMAI, a real-time machine learning classifier deployed within the Fink broker that uses photometric features from ZTF alerts to efficiently identify rare superluminous supernovae candidates without requiring spectroscopic redshift, achieving high recovery rates in initial evaluations and preparing for future application to the Vera C. Rubin Observatory's Legacy Survey of Space and Time.
This paper introduces an attention-based stochastic simulator that generates spatiotemporally coherent, multisite flood portfolios conditioned on interannual climate variability, effectively bridging the gap between existing climate tools and the interannual-to-decadal horizons required for nonstationary, cascading flood risk assessment in insurance and financial planning.