310 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 compares widely used weighted average and median methods for computing a common mean from independent measurements and proposes a new combined estimate to provide more robust and realistic results, particularly when dealing with small samples, biases, or discrepant data.
This paper reviews the application of the classical Allan variance and its modified versions—specifically weighted, multi-dimensional, and weighted multi-dimensional variants—to analyze noise characteristics in unevenly weighted and multi-dimensional astrometric and geodetic time series.
The paper proposes SREAG, a new method for creating a spherical rectangular equal-area grid that divides a sphere into latitudinal rings of near-constant width and further splits them into equal-area cells, offering superior uniformity, resolution flexibility, and ease of use compared to existing equal-area pixelization techniques.
This paper addresses the two-year gap in the pole coordinate of the IERS C01 polar motion series (1858.9–1860.9) by comparing a parametric astronomical model with a data-driven Singular Spectrum Analysis (SSA) approach, ultimately recommending the SSA method as preferable due to its more complete modeling of polar motion.
This paper introduces a rotation-invariant, end-to-end neural network that jointly learns to transform historical returns and regularize covariance eigenvalues to produce a global minimum-variance portfolio, demonstrating superior out-of-sample performance, robust generalization across different market sizes, and resilience to transaction costs compared to state-of-the-art estimators.
This paper addresses confusion among physicists regarding Poisson sampling results by comparing various techniques and recommending Garwood's confidence intervals as the most consistent and intuitive method for summarizing data.
This paper proposes a principled, end-to-end differentiable framework that combines overprovisioned architectures with sparsification, deep supervision, and depth selection to learn compact, interpretable Kolmogorov-Arnold networks without sacrificing accuracy, thereby resolving the tension between model expressiveness and interpretability in scientific machine learning.
This paper clarifies misconceptions in recent literature by demonstrating that Bayesian statistics inherently provides an automatic Occam's razor that disfavors unnatural, fine-tuned models without requiring aleatoric uncertainty, thereby unifying perspectives across statistics, social sciences, physics, and machine learning.
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.