327 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 presents a methodology using analog-based ensembles in reconstructed phase spaces to demonstrate that while the covariance structure of stochastic processes governs the time-dependent dispersion of trajectories, intermittency is the key factor determining how initial separation influences this dispersion in turbulent dynamics.
This paper demonstrates that a team of large language model (LLM) agents can autonomously generate code to solve complex particle physics data analysis tasks, specifically anomaly detection on the LHC Olympics dataset, achieving performance comparable to human state-of-the-art results.
This paper identifies non-normal eigenvector amplification in multidimensional random multiplicative systems as a distinct and dominant mechanism for generating power-law distributions and criticality, where the non-orthogonality of eigenvectors induces transient growth that increases the effective Lyapunov exponent and lowers the tail exponent, particularly as system dimension increases.
This study utilizes archival GPS carrier phase data to conduct a retrospective search for exotic low-mass fields emitted by the GW170817 binary neutron star merger, finding no significant signal but establishing new 95% confidence-level constraints on the interaction energy scale of quadratic couplings that improve upon existing astrophysical limits.
This paper introduces a hybrid deep learning framework combining an autoencoder and Gaussian processes to model the Point Spread Function (PSF) across the full field of view with higher accuracy than the current state-of-the-art PIFF method, thereby addressing critical limitations for weak gravitational lensing analyses in major cosmological surveys like LSST.
This paper investigates neural scaling laws for boosted jet classification using the JetClass dataset, demonstrating that increasing compute reliably drives performance toward asymptotic limits while revealing how data repetition, input features, and particle multiplicity influence scaling efficiency and effective dataset size.
This paper introduces "potential-energy gating," a physics-based method that modulates observation trust in Bayesian filters according to a system's potential energy landscape, demonstrating significant robustness against outliers and parameter misspecification in bistable stochastic systems where traditional statistical approaches fail.
This paper demonstrates that likelihood-free Simulation-Based Inference, particularly the Neural Posterior Estimation method, offers a computationally efficient and accurate alternative to traditional MCMC for exploring high-dimensional Beyond the Standard Model parameter spaces, successfully inferring posterior distributions for both 5-parameter and 9-parameter pMSSM scenarios including dark matter constraints.
This paper proposes a new class of non-parametric, finite-sample statistical intervals that occupy a middle ground between Bayesian and frequentist approaches by utilizing one-dimensional priors to achieve credible intervals that require assigning at least p% belief after observing the interval but before inspecting the full dataset.
This paper develops a parameter-free Stein-type goodness-of-fit test using symmetric Jacobi polynomials to accurately assess whether finite N-body systems follow their exact compact equilibrium distributions, thereby providing a practical tool for validating kinetic models where the classical Gaussian approximation fails.