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 a Two-State Random Walk, which switches between a continuous-time random walk rest state and a Lévy walk motion state, exhibits a generic coexistence of Joseph, Noah, and Moses effects, revealing that stochastic coupling with a CTRW phase can fundamentally induce heavy-tailed increments and aging in systems where Lévy walks alone possess only the Joseph effect.
This paper presents a Bayesian spectral decomposition framework using Markov Chain Monte Carlo sampling to analyze 6.7-GHz methanol maser G339.884$-$1.259 observations from the Ghana Radio Astronomy Observatory, demonstrating that a Voigt profile model outperforms conventional Gaussian and Lorentzian approaches in accurately resolving seven velocity-coherent components and quantifying uncertainties.
This study utilizes the JAM model and a novel Centrality-Independent Genuine Cumulant Analysis (CIGAR) framework to systematically analyze higher-order proton cumulants in Au+Au collisions at high baryon densities, providing a dynamic non-critical baseline essential for the search for the QCD critical point.
This paper introduces the Wasserstein normalized autoencoder (WNAE), a novel unsupervised anomaly detection model that minimizes the Wasserstein distance between the training data and a Boltzmann distribution of reconstruction errors to effectively identify semivisible jets at the CERN LHC while overcoming the outlier reconstruction failures common in standard autoencoders.
This paper introduces the framework of modern generative networks used in physics for fast surrogates and density estimation, while addressing the challenges in quantifying their accuracy, precision, and statistical power.
This paper reviews the conceptual framework, potential pitfalls, and validation strategies of AI-based model-agnostic search techniques designed to enhance the discovery potential of complex scientific data by prioritizing broad exploration over specific theoretical hypotheses.
This paper proposes a continuous-time causal foundation model that ensures trajectory-law invariance to observation schedules through fine-grid integration with decoupled observations, demonstrating superior performance over naive integration methods across diverse nonlinear dynamics and irregular data scenarios.
This paper investigates neural scaling laws for particle jet generation, confirming logarithmic scaling with model size and validating next-token prediction loss as a proxy for physical accuracy, while observing weaker scaling trends for dataset size and compute due to rapid saturation in autoregressive learning.
The paper introduces `pandemonium`, an R package that facilitates high-dimensional analysis in linked spaces by combining cluster analysis with linked visualizations, such as non-linear dimension reduction and animated tours, to explore relationships between predictors and responses in complex datasets like neural network activations and multivariable physical models.
This paper introduces and validates a Monte Carlo-based inference protocol for generalised spin models (Ising, Blume-Capel, and Blume-Emery-Griffiths) to analyze ordinal questionnaire data, demonstrating that the Blume-Emery-Griffiths model outperforms traditional Gaussian approaches in capturing complex features like multi-modality and outliers, though all models struggle with heavy-tailed distributions.