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 LA-VDM, a landmark-constrained algorithm that accelerates Vector Diffusion Maps through a novel two-stage normalization to handle nonuniform sampling densities, ensuring asymptotic convergence to the connection Laplacian for applications like nonlocal image denoising.
This paper establishes a statistical theory demonstrating that the maximal temporal horizon for learning in gated recurrent neural networks is determined by the interplay between the decay rate of an effective learning rate envelope and the concentration properties of heavy-tailed gradient noise, yielding distinct logarithmic, polynomial, or exponential scaling regimes for learnability.
The paper introduces VecAmpFit, a new vectorized library designed for multidimensional amplitude analyses in high-energy physics, featuring explicit gradient calculation and simultaneous multi-dataset fitting capabilities for the Belle II experiment.
This paper demonstrates that a one-class convolutional autoencoder trained exclusively on ground-state configurations can successfully detect the phase transition of the 3D Ising model and accurately recover its critical temperature and correlation-length exponent without prior knowledge of the system's physical parameters.
This paper proposes a complex network-based framework that transforms irregular event time series into networks to quantify global clustering and identify individual clusters via community detection, thereby revealing local dynamics and time scales obscured by traditional macroscopic methods.
The paper introduces \texttt{alabi}, an open-source Python package that accelerates Bayesian inference for computationally expensive models by employing active learning with Gaussian Process surrogates to iteratively refine posterior predictions, thereby reducing the required number of model evaluations by factors of thousands while maintaining accuracy across complex, high-dimensional problems.
This paper demonstrates that the Particle Transformer (ParT) significantly outperforms traditional BDT-based taggers for jet flavor identification at Higgs factories, achieving a 5–10-fold improvement in / tagging efficiency while also enabling effective strange tagging and quark/antiquark separation through the integration of multivariate hadron identification data.
This paper introduces an adaptive LASSO-based method that accurately distinguishes between pairwise and higher-order interactions in coupled oscillator systems from time series data, outperforming existing baselines in synthetic tests and demonstrating practical utility in analyzing human brain network dynamics.
This paper demonstrates that a Random Forest machine learning model, trained on novel "magnitude rates" derived from early photometric data, significantly improves the early classification rate of rare broad-lined Ic supernovae compared to current methods.
This paper introduces a quantum-inspired tensor network algorithm that computes the discrete Laplace transform by decomposing the non-unitary map into a Damping Transform and a Quantum Fourier Transform within a compressed matrix-product operator, enabling efficient simulations of up to input points and output points on classical hardware.