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 study introduces a computationally efficient framework that utilizes non-interacting electron density and Bayesian active learning to achieve highly accurate, zero-shot extrapolative predictions of alloy properties across vast compositional landscapes, significantly reducing the data requirements for discovering refractory high-entropy alloys.
This paper identifies a novel mechanism called "pseudo-coherence," where non-normal pseudospectral amplification in linearly stable, non-oscillatory stochastic systems drives a sharp transition to intermittent collective temporal organization and irreversible currents without requiring intrinsic oscillators or dynamical instabilities.
This paper introduces two interpretable symbolic machine learning methods, the Symbolic Neural Forecaster (SyNF) and the Symbolic Tree Forecaster (SyTF), which successfully learn explicit algebraic equations to forecast chaotic time series with accuracy competitive to deep learning while providing transparent insights into the underlying dynamics.
This paper introduces Partial Decomposition of Granger Causality (PDGC), a novel framework leveraging Partial Information Decomposition to dissect multivariate spectral Granger causality into unique, redundant, and synergistic components, which was successfully applied to physiological networks to reveal distinct patterns of autonomic dysfunction in patients prone to neurally-mediated syncope.
This paper presents an adaptive entropy-driven sensor selection policy within a camera-LiDAR particle filter that dynamically switches between modalities to optimize tracking accuracy and continuity for single-vessel surveillance, validated through real-world maritime deployment.
This paper proposes a scalable Heterogeneous Graph Neural Network (HGNN) that employs a multi-task learning paradigm to simultaneously perform particle vertex association and graph pruning, thereby significantly improving beauty hadron reconstruction performance and inference efficiency for complex particle collision events at the Large Hadron Collider.
This paper introduces a method that transforms multivariate time series into symbolic sequences to identify statistically significant higher-order dependencies via a Bayesian approach, modeling these motifs as hyperedges in a hypergraph to reveal meaningful interactions in complex systems like neural and social networks.
This paper demonstrates that the Qwen2.5 Large Language Model, when combined with connector networks to form a "Lightcone LLM," can effectively analyze and generate 3D cosmological maps from SKA data, outperforming standard initialization methods and matching dedicated networks of similar size for tasks like parameter regression and lightcone generation.
This paper demonstrates that applying Deep Neural Networks to a 2x2 array of linearly-graded Silicon Photomultipliers significantly improves position resolution and linearity while increasing the number of resolvable pixels by a factor of 5.7 to 12.1 compared to traditional reconstruction methods.
This paper introduces QUnfold, an open-source framework that reformulates the High-Energy Physics unfolding problem as a Quadratic Unconstrained Binary Optimization (QUBO) task, enabling competitive reconstruction accuracy through both classical solvers and quantum-compatible methods like D-Wave's hybrid solver.