arXiv subjects

Vincent Alexander Croft

Publications and source records attributed to Vincent Alexander Croft.

5 recordsLinked to original sources

arXiv2026

ALETHEIA: Autonomous Loop for Experimental Theory and HEP Inference Across-data

ALETHEIA is a self-completing tool for monitoring the learning of manifolds in physics foundation models from data. It provides a method to automatically build physics foundation models for permutation-invariant per-event representations of unknown physics manifolds. This process is demonstrated here for dimension-six Standard Model Effective Field Theory (SMEFT) content of four operators in neutral-current Drell-Yan, whose input is unordered event-level features, and we drive it with an active-learning loop that separates two jobs that the literature usually conflates. Active learning completes a representation: given a fixed operator content, an acquisition rule chooses the working points that pin the model's coefficients fastest. The physics expands it: which new operator to switch on is read from the residual structure, ordered by SMEFT power counting, never guessed by the acquisition. The representation is the ManifoldInformer, a permutation-invariant per-event encoder $\psi_\theta$ pooled into a closed-form ridge head; its latent recovers the analytic morphing tangents ($R^2=0.999$) and curvatures ($R^2=0.954$) of the SMEFT cross section. The loop monitors a residual-operator fingerprint: when a single out-of-span direction dominates, it appends that direction to $\psi_\theta$ ($\psi$-extension) and refits. The acquisition arm unlocks new operators through an Arize-Phoenix span, such that the concepts of ``learning correctly'', in which each extension collapses $\sigma_1$; and ``learned completely'', in which $\sigma_1$ is below the noise floor; are read directly off the monitored trace.

hep-ex↗

arXiv2026

Hypothesis Tests for Observing Quantum Entanglement in HWW at the LHC

We present a novel experimental strategy for testing quantum entanglement in Higgs boson decays to $W$ boson pairs at the Large Hadron Collider. Unlike theoretical approaches that rely on expectation values of Bell operators, which are highly sensitive to outliers and detector effects, we introduce a continuous formulation of the CGLMP inequality that enables standard hypothesis testing between entangled and separable states. To overcome the fundamental challenge of reconstructing invisible neutrino momenta in the $H \rightarrow WW^* \rightarrow \ell\nu\ell\nu$ channel, we employ conditional denoising diffusion probabilistic models (cDDPM), which provide unbiased, multidimensional unfolding applicable to the full measured dataset, including backgrounds. We evaluate the diffusion-based reconstruction against analytical methods through profile likelihood hypothesis tests implemented in RooFit, with systematic uncertainties from background normalisation and unfolding shape fully propagated. Our results demonstrate that the diffusion-based approach enables robust hypothesis testing of quantum entanglement in a realistic collider environment, with 3$\sigma$ evidence of quantum entanglement projected at approximately 555~fb$^{-1}$ and exceeding 5$\sigma$ at 1600~fb$^{-1}$ to be well within the expected limits of the HL-LHC luminosity targets.

hep-ex↗

arXiv2026

Gaussian Process Eigenmodes for Statistical and Systematic Uncertainties in Template Fits

Template histograms are the foundation of statistical inference at the Large Hadron Collider. The HistFactory likelihood encodes template uncertainty through per-bin Barlow-Beeston gamma factors for Monte Carlo statistical error and through interpolation-based modifiers for systematic shape variations. These two mechanisms scale with the number of bins, which becomes problematic for multi-dimensional analyses and for templates constructed from limited Monte Carlo samples. We propose the use of eigenmode decomposition for efficiently estimating statistical and systematic uncertainties when replacing histogram templates with smooth functional representations derived from log-Gaussian Cox process posteriors fitted to the Monte Carlo data. The posterior covariance, augmented by rank-1 updates for each systematic shape variation, provides a unified eigenmode basis that encodes both statistical and systematic template uncertainty. Truncating to the leading eigenmodes replaces the full set of per-bin gamma factors and interpolation parameters with a small number of Gaussian-constrained amplitudes. We prove that this construction contains Barlow-Beeston as a limiting case and that the Gaussian Process posterior variance is bounded above by the Barlow-Beeston variance at every bin.

hep-ex↗

arXiv2026

Blobel's Regularized Unfolding: Eigenmode Decomposition and Automatic Smoothing for Inverse Problems in Particle Physics

This document presents a self-contained treatment of regularized unfolding based on cubic B-spline representations and eigenmode filtering, following the original formulation by Blobel and direct translation of the original implementation in Fortran into a modern format. The method, which has been called by several names under its various historical representations, is named here as Blobel's Regularised Unfolding (BRU). This method differs from conventional histogram-based unfolding approaches in that the true distribution is represented as a smooth function parametrised by spline coefficients, and the regularization operates through an eigenmode decomposition of the curvature penalty relative to the statistical precision. This document describes the mathematical structure of the method, the mechanism by which the regularisation strength is determined automatically from the data, and provides a detailed comparison with standard methods including Tikhonov regularisation based methods, Richardson-Lucy iteration, and naive matrix inversion.

hep-ex↗

arXiv2025

Systematic Uncertainties in Unfolding Considering the Likelihood Formalism

This paper describes the treatment of systematic uncertainties in a Likelihood formalism. RooUnfold, which includes most of the unfolding methods that are commonly used in particle physics, is used to compare a newly implemented method inside this toolkit to existing methods. The interface with the RooFit statistical software package is used for the treatment of systematic uncertainties. The RooUnfold package with RooFit interface, commonly called RooFitUnfold, provides a common interface to unfolding algorithms as well as common uniform methods to evaluate their performance in terms of bias, variance and coverage. This paper exploits this common interface to compare the performance of unfolding with a Tikhonov regularisation term directly in the likelihood with an unfolding method that optimises a Tikhonov regularisation applied separately of the likelihood formalism. Comparisons are made with and without the treatment of (systematic) uncertainties and are applied to an example problem.

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