arXiv subjects

Patrick Barry

Publications and source records attributed to Patrick Barry.

2 recordsLinked to original sources

arXiv2026

Event-Level QCD Inference Framework for Quark-Gluon Imaging

We introduce and demonstrate an event-level analysis framework for quark-gluon imaging. For a first application we use it for the inference of parton distribution functions from synthetic deep inelastic scattering data. This framework removes the need for unfolding of detector effects and the binning of events, and therefore eliminates two key sources of information loss. We contrast this event-level framework with the traditional histogram approach by performing a closure test for parton distribution functions from event data obtained from a known ground truth. In this study we assume a perfect detector, which makes unfolding straightforward. The elimination of binning in the event-level framework is demonstrated to have important benefits over the traditional histogram approach, and performs better in the closure test, particularly for a smaller number of events. For example, defining a mean-squared error distance metric, we find that the event-level framework performs around $35\%$ better than the traditional approach for a moderate number of events. The benefits of an event-level framework should increase for inference associated with 3D quark-gluon imaging, because these differential cross sections are of higher dimension and the comparative number of measured events is significantly reduced.

hep-ph↗

arXiv2025

Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability

In the context of image classification, Concept Bottleneck Models (CBMs) first embed images into a set of human-understandable concepts, followed by an intrinsically interpretable classifier that predicts labels based on these intermediate representations. While CBMs offer a semantically meaningful and interpretable classification pipeline, they often sacrifice predictive performance compared to end-to-end convolutional neural networks. Moreover, the propagation of uncertainty from concept predictions to final label decisions remains underexplored. In this paper, we propose a novel uncertainty-aware and interpretable classifier for the second stage of CBMs. Our method learns a set of binary class-level concept prototypes and uses the distances between predicted concept vectors and each class prototype as both a classification score and a measure of uncertainty. These prototypes also serve as interpretable classification rules, indicating which concepts should be present in an image to justify a specific class prediction. The proposed framework enhances both interpretability and robustness by enabling conformal prediction for uncertain or outlier inputs based on their deviation from the learned binary class-level concept prototypes.

cs.CV↗