General & Applied Physics · Physics & Astronomy
Data Analysis & Statistics
The newest papers in Data Analysis & Statistics that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
45 papers, from 4 September 2026 to 5 November 2026: 45 arXiv preprints.
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Newest papers, page 2
From a Hierarchy of Stochastic Differential Equations to a Hierarchy of Generalized Beta Distributions
We introduce a mean-reverting stochastic differential equation with a three-component stochastic term and show that it generates a hierarchy of steady-state (stationary) distributions.How to scale your HEP ML models: A recipe for robust architecture comparisons at scale
Much of the recent progress in machine learning domains such as language models has come from scaling laws that predict performance as a function of training effort.Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability
Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons (MLPs) require dynamic re-training within an optimization environment.Explainable Molecular Structure Inference from GC--MS with Diffusion Models and LLM Reranking
GC--EI--MS is an important technique for analyzing volatile and semivolatile compounds in complex samples.When Is Accuracy Evidence? A Unified Theory of Generalisation, Validation, and Information Fusion
K-fold cross-validation (CV) is widely used as evidence of out-of-sample performance, although folds are neither independent experiments nor equally informative under heterogeneous data.No Model Required: Text Entropy Rate Filtering Mitigates Iterative Fine-Tuning Collapse
Iterative fine-tuning on synthetic data causes \emph{model collapse}: output diversity narrows as rare patterns are progressively lost, a signature most visible as phrase-level repetition.Scaling Collider Event Generation with Residual-Quantized Tokens
Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider, motivating the development of fast, ML-based…A shape-similarity latent space for fluid interfaces: invertible reduced-order modelling of droplet morphology
A droplet breaks up in tens of microseconds, and a recording captures perhaps a dozen frames.PTED: A multi-dimensional two-sample test for scientific inference and generative machine learning
Two-sample tests are widely applicable in inference and generative modelling, yet users frequently fall back on heuristics and visual inspection due to lack of an accessible test that operates in…A citizen science behavioural dataset of an urban collective-risk dilemma on air pollution mitigation under resource inequality
This article describes a behavioural dataset from a collective-risk dilemma public experiment carried out in Barcelona, Spain, within the citizen science \textit{xAire} air quality project.Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios
We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the computational cost of inference and preserving…Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collider
We propose a training recipe that treats charged-particle trajectory parameter regression on high-energy physics detector data as a sequence-modeling task.Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step.Identifiability Limits of Gravitational Wave Phase Deviations: Multiclass Classification with a Multihead Neural Network
We study how well gravitational-wave phase deviations can be identified using a neural network with classification and regression heads.Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure
Accurate digital maps are essential for Advanced Driver Assistance Systems (ADAS) or Autonomous Driving (AD), providing critical information such as road geometry, traffic signs and speed limits…PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images
Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models.SPIBER: Reconstructing Free Energy Landscapes from Short, Unconverged Trajectories with Generative Flow Networks
Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables.Bayesian classification of astronomical spectra with class uncertainties
We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming…Double descent is the principle of least action
The test error of a model plotted against its number of parameters $d$ falls, peaks when the model can just fit the training data, and falls again, exhibiting the double descent phenomenon.Machine learning kinetics from molecular dynamics data
Most molecular transitions occur on timescales far beyond direct molecular dynamics simulations.
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