arXiv preprints from January 1, 2026 through September 23, 2026 — 04:42:26 EST
Modern machine learning is leading to substantial gains in precision, flexibility, and computational efficiency in fundamental physics. Statistical validation, uncertainty quantification, and robustness assessment are less systematically addressed. The VERaiPHY initiative (Validation & Evaluation for Robust AI in PHYsics) is a series...
Spectral imaging provides chemically specific and spatially resolved analysis of microplastics, but its routine application is hindered by large data volumes, acquisition artefacts, spectral variability, and misidentification of polymers due to alike spectra. This study proposes a multi-level preprocessing and modelling framework for...
One-dimensional Gaussian processes with stationary, integrable kernel functions admit exact or arbitrarily accurate state-space representations, enabling linear-time inference through Kalman filtering and Rauch-Tung-Striebel (RTS) smoothing. However, the RTS smoother requires inversion of predicted state covariance matrices, which can...
In high-dimensional online prediction, the best predictor may depend on only a few features, so regret should scale with sparsity rather than the ambient dimension. Feature priming pursues this goal by estimating feature weights from past data and refitting a minimum-norm predictor on the rescaled design. Warmuth and Amid asked at...
Inverse learning within a statistical framework has a wide range of applications. It has garnered significant attention in machine learning, artificial intelligence, and related fields, where the goal is to infer unknown parameters from indirect and noisy observations. This work investigates the stable approximation of $u^{\dagger}$...
We study uniform sampling of binary matrices with fixed row and column sums, a recurring problem in ecological null models, Rasch-model testing, network analysis, and combinatorics. We propose the Snake algorithm, a rejection-free Markov chain Monte Carlo sampler that grows an alternating path until its first self-intersection and...
Regularized sparse regression has been extensively studied in the offline setting, but online formulation remains relatively under-explored. This gap stems from four key challenges: (i) the infeasibility of dynamically updating the regularization parameter in every online round, (ii) managing storage and memory complexity, (iii)...
Learning restricted Boltzmann machines (RBMs) is computationally challenging because it requires expectations whose exact evaluation is generally intractable. The expectations are typically evaluated using a sampling approximation based on blocked Gibbs sampling (BGS), which is a local Markov chain Monte Carlo transition kernel....
Successive Difference Replication (SDR) is a replication based method of variance estimation introduced by Fay and Train (1995) for estimators based on complex multistage surveys, especially those including a final systematic sampling stage. The method has been used for many years as the primary variance-estimation methodology in...
Data-driven algorithm design frames hyperparameter tuning as a statistical learning problem, but establishing generalization guarantees remains challenging due to the implicit, non-smooth dependence of model performance on hyperparameters. Existing multi-dimensional bounds under piecewise-polynomial assumptions remain theoretically...
Modern probabilistic time-series forecasters often express uncertainty through forecast samples. While typically converted into nominal prediction regions using empirical quantiles, these model-implied sets lack formal coverage guarantees and frequently deviate from nominal targets under distribution shift. Existing multivariate...
In large-scale epidemiological studies, it is often of interest to investigate joint relationships between longitudinal and time-to-event outcomes with exposures that are trajectories or functions. Our motivation study is the Objective Physical Activity and Cardiovascular Health (OPACH) Study, which collected accelerometry-measured...
Large twist angles in twisted bilayer graphene are widely expected to be electronically trivial, with negligible interlayer coupling and no electronic reconstruction, in contrast to the rich moiré-driven band reconstruction and correlated physics that emerge at small twist angles. Here, we show that this paradigm breaks down near a...
We describe the upgraded Polarimeter using Imaging CMOS Sensor and Rotating Retarder 2 (PICSARR-2), describe its applications, and characterize its performance for stellar polarimetry on a 36-inch and 14-inch telescope. On the larger telescope in the SDSS $g^\prime$, $r^\prime$ and $i^\prime$ filters a precision of $σ_p=$ 5.7 ppm on...
Real-time simulations of interacting nanostructures coupled to fermionic reservoirs can require a growing number of environmental degrees of freedom to retain long-lived correlations. We introduce tape-recorder coarse graining, which reorganizes each noninteracting lead into incoming, active, and outgoing modes. The device is...
In this study, we use the Bayesian methods in the Enterprise package to examine the fully general parameterization of pulsar timing models in tandem with noise. We investigate four pulsars, PSR J1600$-$3053, PSR J2043+1711, PSR J0740+6620, and PSR J1640+2224, through the lens of Bayesian timing. These four are selected as they are...
As quantum processors scale to hundreds of qubits, execution speed is a critical performance dimension alongside scale and quality. While substantial progress has been made in benchmarking circuit fidelity, existing speed metrics often fail to reflect the sustained, end-to-end throughput experienced by users running utility-scale...
We study the dissipative dynamics of a Majorana tetron qubit in the presence of extrinsic quasiparticle poisoning due to the coupling to external leads. From the Bloch-Redfield equation describing a finite-size topological superconductor hosting four Majorana zero modes, we recover analytical expressions for the steady state, the...
We present a density functional-based equation of state for warm, dense nuclear matter with a transition to deconfined quark matter for applications to simulations of supernova explosions and neutron star mergers, but also for the cosmological evolution of Q-balls. For the quark matter equation of state, we employ a recently developed...
Based on archival spectra, we derive the quiescent instantaneous mass transfer rates in novae using synthetic disk spectra generated with tlusty, Gaia parallax-derived distances, and updated color excess values. Our results for nine novae, based on ultraviolet spectra and on a number of optical spectra, yield mass accretion rates that...
Variations in dynamical states of galaxy clusters can introduce biases and scatter in observable-mass relations. The dynamical state of a cluster is an emergent feature of its mass accretion history (MAH), it is therefore useful to constrain the MAH of the cluster. In this work, we characterize 305 massive clusters from The300 project...
The familiar Bethe-Heitler process on the proton target $e+p\to e+p+γ$ is transformed into a laboratory for studying multiparticle entanglement. We discuss how bipartite and genuine tripartite entanglement between the final state electron, proton and photon are built up by successive $1\to 2$ and $2\to 2$ elementary interactions. We...
High-energy $γγ$ collisions, realizable as an operational mode of future lepton linear colliders such as the ILC and CLIC, provide a promising environment to probe extended Higgs sectors. We investigate the sensitivity of such colliders to doubly charged Higgs bosons within the 2-Higgs Doublet Model with type-II seesaw (2HDMcT)....
The Light-Heavy (LH) model involves two species of particles (light and heavy) coupled with a fluctuating surface (described by tilts). The dynamics include the inherent diffusion of the particles (or tilts) as well as the drive provided by the tilts (or particles). When the two are of similar magnitude, the system lies in the...
Two-dimensional transition-metal dichalcogenide alloys are potential candidates for advanced optoelectronic and neuromorphic applications due to their strong light-matter interactions and controllable defect properties. However, large-area growth of such alloys remains challenging, while the correlation between their physical and...