Article-Journal

Electroweak diboson production in association with a high-mass dijet system in semileptonic final states from $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector. featured image

Observation of vector boson scattering

This paper reports the observation of electroweak diboson ($WW/WZ/ZZ$) production in association with a high-mass dijet system, in which final states with one boson decaying …

Atlas Collaboration
Evidential deep learning for uncertainty quantification and out-of-distribution detection in jet identification using deep neural networks featured image

Evidential DL for Uncertainties and Anomaly Detection

Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In …

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Mark Neubauer

Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning

Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, …

Jieun Yoo

Muon Collider Forum Report

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K. M. Black

Making digital objects FAIR in high energy physics: An implementation for Universal FeynRules Output models

Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation …

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Mark Neubauer

FAIR for AI: An interdisciplinary and international community building perspective

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E. A. Huerta

FAIR AI Models in High Energy Physics

The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate …

J. Duarte

Computational Frontier Topical Group Report Storage and Processing Resource Access

Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation …

W. Bhimji
A detailed study of interpretability of deep neural network based top taggers featured image

Explainability of Deep Neural Networks in top quark tagging

Recent developments in the methods of explainable AI (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about …

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Mark Neubauer

A FAIR and AI-ready Higgs boson decay dataset

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and …

Yifan Chen