Kinematic variables and feature engineering for particle phenomenology
Kinematic variables play an important role in collider phenomenology, as they expedite
discoveries of new particles by separating signal events from unwanted background events …
discoveries of new particles by separating signal events from unwanted background events …
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 …
How to understand limitations of generative networks
Well-trained classifiers and their complete weight distributions provide us with a well-
motivated and practicable method to test generative networks in particle physics. We …
motivated and practicable method to test generative networks in particle physics. We …
Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm
A bstract Hadronic signals of new-physics origin at the Large Hadron Collider can remain
hidden within the copiously produced hadronic jets. Unveiling such signatures require …
hidden within the copiously produced hadronic jets. Unveiling such signatures require …
Creating simple, interpretable anomaly detectors for new physics in jet substructure
Anomaly detection with convolutional autoencoders is a popular method to search for new
physics in a model-agnostic manner. These techniques are powerful, but they are still a …
physics in a model-agnostic manner. These techniques are powerful, but they are still a …
Deep learning jet modifications in heavy-ion collisions
A bstract Jet interactions in a hot QCD medium created in heavy-ion collisions are
conventionally assessed by measuring the modification of the distributions of jet …
conventionally assessed by measuring the modification of the distributions of jet …
Snowmass 2021 computational frontier CompF03 topical group report: Machine learning
P Shanahan, K Terao, D Whiteson - arXiv preprint arXiv:2209.07559, 2022 - arxiv.org
The rapidly-developing intersection of machine learning (ML) with high-energy physics
(HEP) presents both opportunities and challenges to our community. Far beyond …
(HEP) presents both opportunities and challenges to our community. Far beyond …
Equivariant energy flow networks for jet tagging
MJ Dolan, A Ore - Physical Review D, 2021 - APS
Jet tagging techniques that make use of deep learning show great potential for improving
physics analyses at colliders. One such method is the energy flow network (EFN)—a …
physics analyses at colliders. One such method is the energy flow network (EFN)—a …
The information content of jet quenching and machine learning assisted observable design
A bstract Jets produced in high-energy heavy-ion collisions are modified compared to those
in proton-proton collisions due to their interaction with the deconfined, strongly-coupled …
in proton-proton collisions due to their interaction with the deconfined, strongly-coupled …
[图书][B] Advances in Jet Substructure at the LHC
R Kogler - 2021 - Springer
This book has been written as part of my habilitation at the University of Hamburg. It is
intended to serve graduate students and researchers to get familiar with the stateof-the-art of …
intended to serve graduate students and researchers to get familiar with the stateof-the-art of …