From raw detector activations to reconstructed particles, data at the Large Hadron Collider
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …

Jet tagging is a critical yet challenging classification task in particle physics. While deep
learning has transformed jet tagging and significantly improved performance, the lack of a …

The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-
flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable …

A bstract Deep learning methods have been increasingly adopted to study jets in particle
physics. Since symmetry-preserving behavior has been shown to be an important factor for …

In this paper, we present a new method to efficiently generate jets in High Energy Physics
called PC-JeDi. This method utilises score-based diffusion models in conjunction with …

A bstract We devise an autoencoder based strategy to facilitate anomaly detection for
boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known …

We present a new approach, the Topograph, which reconstructs underlying physics
processes, including the intermediary particles, by leveraging underlying priors from the …

A bstract We explore machine learning-based jet and event identification at the future
Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers …

A bstract Constraints on quartic interactions of the Higgs boson with gauge bosons have
been obtained by the experimental LHC collaborations focussing on the so-called κ …

A bstract We show how weakly supervised machine learning can improve the sensitivity of
LHC mono-jet searches to new physics models with anomalous jet dynamics. The …