Machine learning in the search for new fundamental physics
Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …
established and tested standard model of particle physics. Various current and upcoming …
Machine learning and the physical sciences
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …
for a vast array of data processing tasks, which has entered most scientific disciplines in …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Jet tagging via particle clouds
H Qu, L Gouskos - Physical Review D, 2020 - APS
How to represent a jet is at the core of machine learning on jet physics. Inspired by the
notion of point clouds, we propose a new approach that considers a jet as an unordered set …
notion of point clouds, we propose a new approach that considers a jet as an unordered set …
Deep learning and its application to LHC physics
D Guest, K Cranmer, D Whiteson - Annual Review of Nuclear …, 2018 - annualreviews.org
Machine learning has played an important role in the analysis of high-energy physics data
for decades. The emergence of deep learning in 2012 allowed for machine learning tools …
for decades. The emergence of deep learning in 2012 allowed for machine learning tools …
Machine learning at the energy and intensity frontiers of particle physics
Our knowledge of the fundamental particles of nature and their interactions is summarized
by the standard model of particle physics. Advancing our understanding in this field has …
by the standard model of particle physics. Advancing our understanding in this field has …
Jet substructure at the Large Hadron Collider: a review of recent advances in theory and machine learning
AJ Larkoski, I Moult, B Nachman - Physics Reports, 2020 - Elsevier
Jet substructure has emerged to play a central role at the Large Hadron Collider (LHC),
where it has provided numerous innovative new ways to search for new physics and to …
where it has provided numerous innovative new ways to search for new physics and to …
[HTML][HTML] Energy flow networks: deep sets for particle jets
A bstract A key question for machine learning approaches in particle physics is how to best
represent and learn from collider events. As an event is intrinsically a variable-length …
represent and learn from collider events. As an event is intrinsically a variable-length …
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 …