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 introduces a novel Log Gaussian Cox Process (LGCP) method for modeling smooth backgrounds in high energy physics that minimizes assumptions about the underlying shape by utilizing a Gaussian process for the intensity function and Markov Chain Monte Carlo for optimization, demonstrating its effectiveness through synthetic experiments against traditional analytic functional forms.
This paper addresses the limitations of standard error bar plots when data uncertainties are correlated and proposes enhanced visualization techniques, such as displaying the first principal component and conditional uncertainties, to enable more accurate assessment of model-data agreement.
The paper introduces JetPrism, a configurable Conditional Flow Matching framework that addresses the misleading nature of standard training losses in nuclear physics simulations by establishing a multi-metric evaluation protocol to ensure generative models achieve true physical fidelity and convergence beyond generic loss indicators.
This paper proposes an algebraic filtering method that treats symmetry generators as linear operators to efficiently enumerate all symmetry-allowed terms for continuum equations, thereby providing a systematic and provably complete search space for data-driven governing equation discovery.
By integrating the complete synaptic and neuropeptidergic connectomes of *C. elegans* within a unified multiplex framework grounded in statistical physics, this study reveals how synaptic and extrasynaptic signaling form four complementary communication regimes that collectively optimize brain function for speed, modulation, robustness, and survival.
QuantumXCT is a hybrid quantum-classical generative framework that infers cell-cell communication by learning interaction-induced state transformations via parameterized quantum circuits, enabling the de novo discovery of complex regulatory networks and communication hubs without relying on pre-curated ligand-receptor databases.
This paper proposes a Template-Adapted Mixture Model that leverages multiple biased simulations to perform robust, data-driven parameter inference for signal fraction estimation, effectively mitigating simulation-data mismodeling in complex particle physics analyses.
This paper demonstrates that purely deterministic interactions in the logistic Game of Life can generate scale-invariant dynamics and criticality, revealing two distinct critical points that separate sparse-static, sparse-dynamic, and dense-dynamic phases with unconventional power-law cluster distributions.
This paper presents a method to quantify nonlinear interactions in noisy, partially observable complex systems by transforming the global inference problem into a sequence of solvable tasks using only incomplete statistical snapshots of the system's state.
This paper extends active learning emulators with error estimation to coupled-channel nuclear two-body scattering in momentum space by employing Lippmann-Schwinger-based reduced-order models trained via greedy algorithms, demonstrating high accuracy and computational speedup for phase shifts and cross sections to facilitate future Bayesian calibrations of nuclear interactions.