314 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 proposes two new semi-empirical models for the wall-pressure spectrum in high-Reynolds-number turbulent flows that overcome the limitations of conventional models by combining inner- and outer-scaled spectral components to accurately capture low-frequency growth and variance across a wide range of friction Reynolds numbers.
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.
The paper introduces the FAIR Universe Weak Lensing ML Uncertainty Challenge, a two-phase benchmark initiative designed to advance machine learning methodologies for extracting cosmological parameters from weak lensing data by addressing key challenges such as limited training data, systematic modeling inaccuracies, and distribution shifts.
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 a Functional Renormalization Group framework that detects signal onset in nearly continuous spectra by identifying a "dimensional phase transition" in the spectral geometry, enabling signal detection at ratios significantly lower than traditional BBP thresholds.
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.
This paper presents a comprehensive comparison of gravitational wave inference methods, demonstrating that the Derivative Approximation for Likelihoods (DALI) offers a significantly more accurate and computationally efficient alternative to traditional MCMC and Fisher Matrix approaches, while introducing the public \texttt{GWDALI} code to facilitate rapid and precise posterior estimation for next-generation observatories.
This paper introduces and evaluates a new W-based neutrino energy estimator for liquid-argon time-projection chambers, demonstrating that while it offers superior robustness against modeling uncertainties and minimal bias in the transition region between scattering regimes, its slightly worse energy resolution under ideal conditions suggests a complementary role alongside more exclusive methods for future oscillation analyses.