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 demonstrates that adversarial training enhances the robustness of deep learning-based jet flavor tagging algorithms against input distortions, which serve as a proxy for systematic uncertainties, by leveraging geometric insights from the loss surface to maintain high performance while mitigating model vulnerabilities.
This paper introduces a novel data-driven approach that infers governing non-autonomous dynamical equations by identifying optimal basis functions, successfully reconstructing and predicting the behavior of diverse real-world systems ranging from biological aggregates to engineering applications.
This paper proposes a "Double Metric Learning" approach that learns two distinct node representations to resolve conflicts in constructing directed graphs with chain connections for the ATLAS ITk detector, demonstrating improved performance in graph construction and edge direction prediction for high transverse momentum particles compared to simple metric learning.
This paper demonstrates that adapting the Ant Colony Optimization algorithm to tune the FVC density functional significantly reduces the mean relative error in predicting ground-state energies for strongly correlated systems across various dimensionalities, achieving up to a 67% error reduction with low computational cost.
This paper presents a platform-agnostic Quantum Reservoir Computing framework utilizing a small-scale six-qubit system to achieve over 86% accuracy in forecasting stock trends and trading volumes for quantum-sector companies, demonstrating the potential of near-term quantum hardware for complex financial time-series analysis.
This study mathematically demonstrates that data gaps and outliers significantly compromise the reliability of variance- and autocorrelation-based resilience indicators, with missing values weakening their agreement and outliers causing systematic overestimation of system stability.
This paper demonstrates that the exact marginalization of binary correction variables in physics data is mathematically equivalent to the Ising model, enabling the use of efficient statistical physics tools to handle exponentially complex configurations and accurately quantify uncertainties in applications like Type Ia supernova calibration.
CVEvolve is an autonomous, zero-code agentic system that leverages LLMs and a multi-round search strategy to independently discover and optimize scientific data-processing algorithms, enabling domain scientists to effectively analyze complex, unstructured data without requiring extensive programming expertise.
This paper presents a self-learning signal classifier for decameter coherent scatter radars that automatically constructs a model using two years of data from 12 SuperDARN and SECIRA radars to identify 14 confidently separable classes based on a combination of measured radar parameters and modeled radio wave propagation characteristics.
This paper introduces the Bayes factor-Bayes factor (BB) plot, a diagnostic tool that leverages the relationship between Bayes factors and their distributions under competing hypotheses to validate calculation accuracy and efficiently estimate background distributions, as demonstrated through applications in gravitational wave astronomy including the statistical significance assessment of GW231123.