321 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 a robust framework based on Dempster-Shafer evidence theory that accurately reconstructs complex network topologies from time series data by integrating multi-source information, demonstrating high fidelity and scalability across various network models and real-world datasets.
This paper discusses various internal criteria and influencing factors for assessing the quality of data unfolding results in particle physics and related fields, addressing the challenge of evaluating deconvolution accuracy when external benchmarks are unavailable.
This paper proposes and validates a robust, quantitative method based on the slope of the deterministic term in a Langevin equation to quantify system resilience and detect regime shifts, demonstrating its superior performance over traditional early warning indicators like autocorrelation and standard deviation when applied to seasonal ecological data under strong correlated noise.
This paper introduces a Bayesian Langevin approach to simultaneously quantify deterministic and stochastic dynamics in critical transitions, applying it to the 1996 North American blackout to reveal that a permanent grid state change occurred two minutes before the official trigger, thereby highlighting the necessity of distinguishing destabilizing factors for reliable early warning.
This paper demonstrates that the mean market correlation of the S&P500 exhibits significant non-Markovian memory effects spanning at least three weeks and a hidden slow time scale, which, when modeled via a generalized Langevin equation, significantly improves forecasting accuracy and supports the existence of locally stable market states for optimal portfolio selection.
This paper argues that common statistical corrections for regression to the mean are often flawed or impractical, proposing instead that researchers should evaluate uncorrected data against a structural null expectation derived from measurement repeatability to avoid systematic bias and inflated error rates.
This paper presents a novel, computationally efficient nonlinear unsteady aerodynamic reduced-order model that integrates oscillator dynamics with Volterra theory to accurately reproduce complex aeroelastic shock buffet phenomena, such as lock-in, in three-dimensional transonic flows.
This paper demonstrates that high-accuracy, reference-free material classification using sparse-frequency terahertz spectroscopy can be achieved by applying data-driven feature selection algorithms to identify discriminative absorption bands, thereby eliminating the need for broadband sources and reference measurements for compact sensor applications.
This paper introduces a data-driven protocol that segments non-stationary, non-Gaussian, and heteroskedastic weather data into local pseudo-equilibrium seasons to construct accurate low-dimensional models using state-based generalized master equations, thereby overcoming the limitations of standard generalized Langevin equations in driven, dissipative systems.
This study demonstrates that internal coupling significantly broadens the operational regime and enhances the performance of quantum Otto cycles across equilibrium and non-equilibrium limit cycles, enabling engine or refrigerator functionality in previously inoperable parameter ranges and allowing efficiencies to exceed standard Otto bounds while remaining below the Carnot limit.