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
Between Hi-Fi and MMI: the overlap of redundancy and synergy in partial information decomposition
Partial information decomposition (PID) splits the information that two sources carry about a target into redundant, unique and synergistic parts, with nonnegative decompositions lying on a segment…An automated photometric pipeline for measuring the corotation radius of spiral galaxies
The corotation radius R_CR of a spiral galaxy marks the radius where the angular speed of the spiral pattern matches the orbital speed of the disk material.Anomaly-ranked confirmation slows new-class learning
Anomaly detectors rank unfamiliar examples for expert confirmation, and the confirmed examples teach a classifier to recognise a new class.Beyond Synthesis Conditions: Linking In-situ Growth Dynamics to Carbon Nanotube Forest Properties through Multimodal Machine Learning
Machine learning for chemical vapor deposition (CVD) of carbon nanotubes (CNTs) has largely mapped static synthesis conditions to final properties, implicitly treating these conditions as a complete…Localized anomaly detection with a calorimeter normalizing flow
Normalizing flows trained as fast calorimeter simulators also give the likelihood of each shower, which can serve as an anomaly score.Model-Agnostic Search for New Physics in the Presence of Imperfect Background by Smearing the Data
In the search for new particles in high-energy physics, it is necessary to first define the signal region (SR) enriched in signal events, and then compare events in this region with the expected…Combinatorial Residual Network: a decay-chain-aware classifier without explicit jet assignment
Final states with several jets from the decays of heavy particles pose a combinatorial problem, since the jets must be assigned, at least implicitly, to their parent particles.Converting a superseded Raith electron beam pattern generator into an unattended Critical Dimension SEM
Design-based metrology of academic electron beam lithography (EBL) is bottlenecked by low-throughput imaging.Hyperspectral Unmixing using a Structured Linear Mixing Model with Additive Spectral Variability
Linear hyperspectral unmixing aims at retrieving pure material spectra present in an observed scene and their associated abundance fractions in each pixel.Improving Signals, Declining Encoder: A Known-Answer Test of Label-Free Monitoring in Self-Supervised Learning
Self-supervised training is monitored without labels, through the loss and statistics of the learned features.Shared Is Not Enough: Predictor Width and Factor Readability Under Masked Latent Prediction
Masked latent prediction methods such as I-JEPA aim to learn predictable features, but what determines which information they retain is less clear.Track finding and particle identification performance with particle physics foundation models
Modern high energy nuclear and particle physics experiments produce large amounts of sparse data that must be reduced significantly in size before performing physics analysis.Early learning in large residual networks
We study the early training dynamics of nonlinear residual networks with blocks of zero hidden layers as a model of more general residual networks.How Far Can Foundation Models Be Distilled for Efficient Jet Tagging?
Jet tagging is one of the most important applications of machine learning at the Large Hadron Collider.Phase Transitions in Learning Driven by Extremal Data
We study an extremal regression problem, an example of multiple instance learning (MIL), where the predictors are given as multisets, called bags.Implicit and explicit reparameterizations of the Folded Normal and Rice distributions
Variational inference is a flexible tool for modeling experimental data and has proven useful in crystallography.Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC
Anomaly detection, the identification of collision events that deviate from Standard Model expectations, is one of the most promising model-agnostic strategies for new-physics searches at the Large…A data-driven heuristic-ensemble method for behavioural inference in collective-risk dilemma experiments
The collective-risk dilemma has become a paradigm for studying cooperation in a wide variety of social experiments, initiatives that generate increasingly rich datasets describing individual actions…Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals
Physical reservoir computing exploits the nonlinear dynamics of physical systems to process time-dependent data with greater energy efficiency than conventional machine learning approaches.Progress and Prospect of AI in ARPES Workflow
Artificial intelligence (AI) is becoming an increasingly useful tool across the experimental sciences, including angle-resolved photoemission spectroscopy (ARPES), which routinely produces large…
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