scholarly journals Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning

2020 ◽  
Vol 841 ◽  
pp. 1-63 ◽  
Author(s):  
Andrew J. Larkoski ◽  
Ian Moult ◽  
Benjamin Nachman
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
Jet Substructure ◽  
Recent Advances

scholarly journals Utilizing Unsupervised Machine Learning in BSM Physics Searches At The LHC

2020 ◽  
Vol 245 ◽  
pp. 06021
Author(s):  
Adam Leinweber ◽  
Martin White
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
New Physics ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique ◽  
Bsm Physics ◽  
Supersymmetric Particles ◽  
One Machine

Recent searches for supersymmetric particles at the Large Hadron Collider have been unsuccessful in detecting any BSM physics. This is partially because the exact masses of supersymmetric particles are not known, and as such, searching for them is very difficult. The method broadly used in searching for new physics requires one to optimise on the signal being searched for, potentially suppressing sensitivity to new physics which may actually be present that does not resemble the chosen signal. The problem with this approach is that, in order to detect something with this method, one must already know what to look for. I will showcase one machine-learning technique that can be used to define a “signal-agnostic” search. This is a search that does not make any assumptions about the signal being searched for, allowing it to detect a signal in a more general way. This method is applied to simulated BSM physics data and the results are explored.

scholarly journals Jet Substructure as a New Higgs-Search Channel at the Large Hadron Collider

2008 ◽  
Vol 100 (24) ◽  
Author(s):  
Jonathan M. Butterworth ◽  
Adam R. Davison ◽  
Mathieu Rubin ◽  
Gavin P. Salam
Keyword(s):  
Large Hadron Collider ◽  
Hadron Collider ◽  
Jet Substructure ◽  
Higgs Search

scholarly journals Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 53
Author(s):  
Massimo Giovannozzi ◽  
Ewen Maclean ◽  
Carlo Emilio Montanari ◽  
Gianluca Valentino ◽  
Frederik F. Van der Veken
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Performance Optimization ◽  
Hadron Collider ◽  
High Energy ◽  
Beam Dynamics ◽  
Machine Learning Techniques ◽  
Nonlinear Beam ◽  
Dynamic Aperture ◽  
Advanced Analysis

A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, Machine Learning has been applied to Accelerator Physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, Machine Learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of Machine Learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe Machine Learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture.

scholarly journals Application of machine learning techniques at the CERN Large Hadron Collider

2020 ◽  
Author(s):  
Frederik Van Der Veken ◽  
Gabriella Azzopardi ◽  
Fred Blanc ◽  
Loic Coyle ◽  
Elena Fol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
Machine Learning Techniques ◽  
Cern Large Hadron Collider ◽  
Learning Techniques

scholarly journals Jet substructure at the Large Hadron Collider

2019 ◽  
Vol 91 (4) ◽  
Author(s):  
Roman Kogler ◽  
Benjamin Nachman ◽  
Alexander Schmidt ◽  
Lily Asquith ◽  
Emma Winkels ◽  
...  
Keyword(s):  
Large Hadron Collider ◽  
Hadron Collider ◽  
Jet Substructure

scholarly journals Machine learning in accelerator physics: applications at the CERN Large Hadron Collider

2020 ◽  
Author(s):  
Frederik Van Der Veken ◽  
Gabriella Azzopardi ◽  
Fred Blanc ◽  
Loic Coyle ◽  
Elena Fol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
Accelerator Physics ◽  
Cern Large Hadron Collider
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