Foundations of automatic feature extraction at LHC–point clouds and graphs

Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new al...

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Published in:The European physical journal. ST, Special topics Vol. 233; no. 15-16; pp. 2619 - 2640
Main Authors: Bhardwaj, Akanksha, Konar, Partha, Ngairangbam, Vishal
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Springer Science + Business Media
Subjects:
Algorithms
Approximation
Atomic
Classical and Continuum Physics
Condensed Matter Physics
Datasets
Deep learning
Feature extraction
Graphical representations
Large Hadron Collider
Machine learning
Materials Science
Measurement Science and Instrumentation
Modern Machine Learning and Particle Physics: An In-Depth Review
Molecular
Neural networks
Optical and Plasma Physics
Phenomenology
Physics
Physics and Astronomy
Review
Sensors
Variables
ISSN:1951-6355, 1951-6401
Online Access:Get full text
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Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.
AbstractList Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as "replacing the expert" in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.
Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as "replacing the expert" in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as "replacing the expert" in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.
Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.
Author Konar, Partha
Ngairangbam, Vishal
Bhardwaj, Akanksha
Author_xml – sequence: 1
  givenname: Akanksha
  surname: Bhardwaj
  fullname: Bhardwaj, Akanksha
  organization: Department of Physics, Oklahoma State University
– sequence: 2
  givenname: Partha
  surname: Konar
  fullname: Konar, Partha
  organization: Theoretical Physics Division, Physical Research Laboratory
– sequence: 3
  givenname: Vishal
  orcidid: 0000-0002-7143-715X
  surname: Ngairangbam
  fullname: Ngairangbam, Vishal
  email: vishal.s.ngairangbam@durham.ac.uk
  organization: Institute for Particle Physics Phenomenology, Department of Physics, Durham University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/39605978$$D View this record in MEDLINE/PubMed
https://www.osti.gov/biblio/2478724$$D View this record in Osti.gov
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Snippet Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and...
Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from...
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StartPage 2619
SubjectTerms Algorithms
Approximation
Atomic
Classical and Continuum Physics
Condensed Matter Physics
Datasets
Deep learning
Feature extraction
Graphical representations
Large Hadron Collider
Machine learning
Materials Science
Measurement Science and Instrumentation
Modern Machine Learning and Particle Physics: An In-Depth Review
Molecular
Neural networks
Optical and Plasma Physics
Phenomenology
Physics
Physics and Astronomy
Review
Sensors
Variables
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Title Foundations of automatic feature extraction at LHC–point clouds and graphs
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