312 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 demonstrates that accurately representing deep ocean features, particularly mesoscale eddies, in initial conditions is critical for improving ensemble forecast performance of surface fields in the Gulf of Mexico, thereby motivating the assimilation of deep observations to better constrain full-water-column circulation.
This paper introduces an axiom stating that entropic parameters cannot be inferred from uniform distributions, which uniquely selects Rényi entropy among generalized families and enables the consistent, data-driven estimation of these parameters while ensuring that the maximized log-likelihood always equals the negative Shannon entropy.
This study reveals that early-career brokerage in physics creates a cumulative advantage that amplifies career success and inequality for all researchers regardless of gender, suggesting that promoting such opportunities early on could help mitigate these disparities.
This paper demonstrates that quantum generative adversarial networks (QGANs), utilizing quantum correlations and simulated via full circuit or tensor network methods, can effectively generate synthetic financial time series that successfully replicate both target distributions and essential temporal correlations.
This paper introduces the PATCH model to demonstrate how the interplay of preferential attachment, homophily, and triadic closure drives network inequalities, revealing that while the first two mechanisms exacerbate segregation and degree disparities, triadic closure uniquely reduces between-group inequality while amplifying overall degree inequality, a framework validated by fifty years of gender disparities in Physics and Computer Science collaboration networks.
This paper systematically evaluates over 6,000 time-series statistics across 35 diverse systems to identify effective data-driven methods for detecting temporal asymmetry, revealing that while no single metric universally captures all forms of irreversibility, specific families of statistics can successfully distinguish irreversible dynamics when tailored to the system's characteristics.
This paper introduces PRL-Bench, a comprehensive benchmark derived from recent Physical Review Letters papers that evaluates the capabilities of large language models in conducting end-to-end, autonomous physics research across five subfields, revealing a significant performance gap between current AI systems and the demands of real-world scientific discovery.
Using the latest LIGO-Virgo-KAGRA data, this paper demonstrates that hierarchical population-informed parameter estimation is essential for interpreting gravitational-wave observations, as standard methods relying on unphysical reference priors yield generically biased source properties unsuitable for direct astrophysical analysis.
This paper presents the first comprehensive evaluation and ranking of 13 explainable AI (XAI) methods across 24 metrics for head and neck cancer outcome prediction, demonstrating that Integrated Gradients and DeepLIFT consistently achieve high performance in faithfulness, complexity, and plausibility on the HECKTOR dataset.