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 introduces a maximum likelihood estimation method to infer burst-merging kernels from bursty time series, enabling the precise characterization of their hierarchical burst structures and underlying mechanisms through both synthetic and empirical data validation.
FluxMC is a machine learning-enhanced framework that combines Flow Matching with Parallel Tempering MCMC to overcome the computational bottlenecks of traditional Bayesian inference, enabling rapid and high-fidelity parameter estimation for space-based gravitational-wave observations without compromising between model accuracy and analysis speed.
Using random matrix theory, this paper demonstrates that detecting shared signals in undersampled high-dimensional data is significantly enhanced by utilizing cross or joint covariance matrices rather than individual self-covariances, with the optimal choice depending on the dimensional mismatch between the variables.
This review explores how emerging artificial intelligence techniques can overcome long-standing limitations in multi-component models by strengthening cross-domain interactions and enabling more coherent, unified Earth system coupling frameworks.
This paper demonstrates that probabilistic neural networks can effectively replace Gaussian processes in Pulsar Timing Array analyses of the gravitational-wave background, yielding consistent posterior results while significantly reducing computational training and inference runtimes.
This study demonstrates that heterogeneous swarms combining exploratory and exploitative agents outperform homogeneous groups in locating odor sources within 3-D turbulent environments by effectively mitigating signal spatial correlations, offering new insights for both biological collective behavior and bioinspired engineering algorithms.
This paper generalizes the decoupling of internal fluctuating variables from cell size in size-structured populations by deriving conditions for lineage and ensemble decoupling, connecting these results to the Feynman-Kac formula to transform size dynamics into growth-homogeneous processes via random time changes and exponential tilting.
This paper demonstrates that Neural Posterior Estimation (NPE) offers a scalable, real-time alternative to traditional Bayesian calibration for Li-ion battery parameter inference, achieving comparable or superior accuracy and interpretability across high-dimensional cases while shifting computational costs to the training phase.
This paper introduces a novel Log Gaussian Cox Process (LGCP) method for modeling smooth backgrounds in high energy physics that minimizes assumptions about the underlying shape by utilizing a Gaussian process for the intensity function and Markov Chain Monte Carlo for optimization, demonstrating its effectiveness through synthetic experiments against traditional analytic functional forms.
This paper addresses the limitations of standard error bar plots when data uncertainties are correlated and proposes enhanced visualization techniques, such as displaying the first principal component and conditional uncertainties, to enable more accurate assessment of model-data agreement.