316 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 data-driven "structured generalized sliced Wasserstein distance" method using randomized neural networks to directly analyze two-dimensional polarized images from Gas Pixel Detectors, successfully determining X-ray polarization and incident angles while demonstrating high consistency with traditional statistical models.
This paper introduces a spectral approach to the 3D Edwards-Anderson spin glass, demonstrating that the eigenvalue statistics of overlap matrices exhibit a q-Gaussian crossover from a Wigner semicircle to a Gaussian distribution at the critical temperature, thereby offering a robust and computationally efficient indicator of spin-glass criticality.
The paper introduces O-Sensing, a protocol that uses parsimony-driven optimization and spectral entropy maximization to reconstruct the Hamiltonian, interaction geometry, and symmetries of a quantum many-body system directly from a few low-lying eigenstates, even when the underlying connectivity is unknown.
This paper proposes a new ensemble filtering approach that inherently localizes the analysis probability density function via variational Bayesian optimization and marginal partitioning, thereby eliminating the need for ad-hoc localization techniques while achieving accuracy and computational efficiency comparable to tuned standard EnKF and ETKF methods.
This paper identifies that measurement-feedback Ising machines suffer from a significantly reduced range of effective hyperparameters compared to their time-continuous analog counterparts due to discrete operation, and proposes an experimentally verified method to mitigate this sensitivity.
This paper argues that true abstraction in neural networks depends not only on depth but also on the breadth of the training data, proposing a renormalisation group framework where expanding the data scope leads representations toward a unique "Hierarchical Feature Model," a hypothesis validated by experiments with Deep Belief Networks and auto-encoders.
This paper proposes a robust framework based on Dempster-Shafer evidence theory that accurately reconstructs complex network topologies from time series data by integrating multi-source information, demonstrating high fidelity and scalability across various network models and real-world datasets.
This paper discusses various internal criteria and influencing factors for assessing the quality of data unfolding results in particle physics and related fields, addressing the challenge of evaluating deconvolution accuracy when external benchmarks are unavailable.
This paper proposes and validates a robust, quantitative method based on the slope of the deterministic term in a Langevin equation to quantify system resilience and detect regime shifts, demonstrating its superior performance over traditional early warning indicators like autocorrelation and standard deviation when applied to seasonal ecological data under strong correlated noise.
This paper introduces a Bayesian Langevin approach to simultaneously quantify deterministic and stochastic dynamics in critical transitions, applying it to the 1996 North American blackout to reveal that a permanent grid state change occurred two minutes before the official trigger, thereby highlighting the necessity of distinguishing destabilizing factors for reliable early warning.