Exploring QCD matter in extreme conditions with Machine Learning
In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …
problem-solving perspective for physics, offering new avenues for studying strongly …
Particle transformer for jet tagging
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 …
learning has transformed jet tagging and significantly improved performance, the lack of a …
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 …
An efficient Lorentz equivariant graph neural network for jet tagging
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 …
physics. Since symmetry-preserving behavior has been shown to be an important factor for …
Precision QCD, hadronic structure & forward QCD, heavy ions: report of energy frontier topical groups 5, 6, 7 submitted to snowmass 2021
This report was prepared on behalf of three Energy Frontier Topical Groups of the
Snowmass 2021 Community Planning Exercise. It summarizes the status and implications of …
Snowmass 2021 Community Planning Exercise. It summarizes the status and implications of …
A fragmentation approach to jet flavor
S Caletti, AJ Larkoski, S Marzani, D Reichelt - Journal of High Energy …, 2022 - Springer
A bstract An intuitive definition of the partonic flavor of a jet in quantum chromodynamics is
often only well-defined in the deep ultraviolet, where the strong force becomes a free theory …
often only well-defined in the deep ultraviolet, where the strong force becomes a free theory …
Equivariant, safe and sensitive—graph networks for new physics
A bstract This study introduces a novel Graph Neural Network (GNN) architecture that
leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of …
leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of …
Lund multiplicity in QCD jets
A bstract We compute the average Lund multiplicity of high-energy QCD jets. This extends
an earlier calculation, done for event-wide multiplicity in e+ e− collisions [1], to the large …
an earlier calculation, done for event-wide multiplicity in e+ e− collisions [1], to the large …
Diffusion model approach to simulating electron-proton scattering events
Generative artificial intelligence is a fast-growing area of research offering various avenues
for exploration in high-energy nuclear physics. In this work, we explore the use of generative …
for exploration in high-energy nuclear physics. In this work, we explore the use of generative …
Lund and Cambridge multiplicities for precision physics
A bstract We revisit the calculation of the average jet multiplicity in high-energy collisions.
First, we introduce a new definition of (sub) jet multiplicity based on Lund declusterings …
First, we introduce a new definition of (sub) jet multiplicity based on Lund declusterings …