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 proposes a renormalization group framework that utilizes a size-dependent normalization to collapse eigenvalue densities of random matrix ensembles with power-law variance profiles, deriving fixed-point equations and Beta functions to demonstrate spectral collapse across different system sizes.
This study demonstrates that an unsupervised machine learning framework, combining Isolation Forest, PCA reconstruction error, and DBSCAN, effectively identifies specific heavy metal contamination anomalies in Ghanaian soils that correlate strongly with elevated health risks, thereby enabling more targeted environmental management than traditional aggregate indices alone.
This paper introduces a physically-informed fuzzy clustering method using an expectation-maximization algorithm and modified Bayesian information criterion to automatically determine the optimal number of tracks and separate vertical sounding ionograms, even under disturbed ionospheric conditions, by incorporating adaptive noise filtering and extraordinary mode removal.
This paper introduces the Causal Edge Rees Algebra (CERA), a novel algebraic framework that encodes the causal evolution of connectivity in spatiotemporal graphs by associating a temporal filtration of edge ideals to a single graded object, thereby enabling the identification of critical structural bridges and offering a new perspective on causal network dynamics distinct from geometric topological data analysis.
This paper presents a refined procedure for estimating uncertainties in the molecular final-state distribution of tritium beta decay, which significantly reduces the associated systematic uncertainty on the squared neutrino mass from 0.02 eV²/c⁴ to 0.0013 eV²/c⁴, thereby enhancing the precision of the KATRIN experiment's neutrino-mass measurement.
By applying physically motivated priors to all distances in a comprehensive Bayesian recalibration of the local distance ladder, this study demonstrates that the assumed priors significantly lower the inferred Hubble constant to , thereby reducing the Hubble tension from to .
This paper introduces "joint dynamic programming," a co-design framework that simultaneously optimizes continuous hardware geometry and adaptive measurement policies to significantly outperform traditional non-adaptive or separately optimized approaches in sensing tasks, as demonstrated by substantial error reductions across radar, quantum, and photonic sensor case studies.
This paper proposes a Hopfield-type associative memory model where astrocyte-mediated, entropy-regularized gain modulation dynamically implements self-attention mechanisms, thereby significantly improving retrieval accuracy under high memory loads compared to classical approaches.
This paper introduces a response-targeted method called emulator-based component analysis to decompose the statistical variability in simulated UV-visible spectra, demonstrating its ability to identify the specific structural features of *trans*-azobenzene in ethanol that drive spectral changes during photoexcitation.
This paper proposes a multi-fidelity surrogate modeling framework using recursive co-kriging to accurately and efficiently predict wind-load coefficients on modern container ships by combining empirical correlations with simplified and detailed CFD models across various harbor environments.