Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC
S Mondal, L Mastrolorenzo - The European Physical Journal Special …, 2024 - Springer
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 …
flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable …
Jet substructure
L Apolinário, YT Chien, LC Mendez - International Journal of …, 2024 - World Scientific
Jet substructure observables hold the keys to identifying the inner working of the quark–
gluon plasma through its imprints in jet modification patterns. Because of the multi-scale …
gluon plasma through its imprints in jet modification patterns. Because of the multi-scale …
A spectral metric for collider geometry
A bstract By quantifying the distance between two collider events, one can triangulate a
metric space and reframe collider data analysis as computational geometry. One popular …
metric space and reframe collider data analysis as computational geometry. One popular …
QCD masterclass lectures on jet physics and machine learning
AJ Larkoski - The European Physical Journal C, 2024 - Springer
These lectures were presented at the 2024 QCD Masterclass in Saint-Jacut-de-la-Mer,
France. They introduce and review fundamental theorems and principles of machine …
France. They introduce and review fundamental theorems and principles of machine …
PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts
S Mondal, G Barone, A Schmidt - Journal of High Energy Physics, 2024 - Springer
A bstract We propose a new approach of jet-based event reconstruction that aims to
optimally exploit correlations between the products of a hadronic multi-pronged decay …
optimally exploit correlations between the products of a hadronic multi-pronged decay …
SPECTER: efficient evaluation of the spectral EMD
A bstract The Energy Mover's Distance (EMD) has seen use in collider physics as a metric
between events and as a geometric method of defining infrared and collinear safe …
between events and as a geometric method of defining infrared and collinear safe …
A Step Toward Interpretability: Smearing the Likelihood
AJ Larkoski - arxiv preprint arxiv:2501.07643, 2025 - arxiv.org
The problem of interpretability of machine learning architecture in particle physics has no
agreed-upon definition, much less any proposed solution. We present a first modest step …
agreed-upon definition, much less any proposed solution. We present a first modest step …