316 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 network reconstruction method that extends the Fitness Model by incorporating block structures via a Jeffreys prior to handle missing block-specific density data, demonstrating superior performance and reduced overfitting on international trade datasets compared to both block-agnostic and information-rich baseline models.
This study demonstrates that while Gaussian process machine learning effectively predicts lattice parameters and compositions for ZrO-based shape memory ceramics, the metal alloy-derived design criteria used to identify a low-hysteresis candidate failed to account for critical ceramic-specific factors, resulting in a composition with unexpectedly high thermal hysteresis.
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 paper proposes a GPU-accelerated Sequential Monte Carlo method that achieves over 500x speedup compared to CPU-based approaches, enabling efficient Bayesian model selection and parameter estimation for complex spectral data analysis in XPS and XRD applications.
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.
The paper introduces PATHFINDER, an autonomous microscopy framework that integrates novelty-driven exploration with multi-objective optimization to efficiently discover diverse and scientifically significant structural and spectral states while avoiding premature convergence on single solutions.
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.