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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Vydáno v:	The European physical journal. ST, Special topics Ročník 233; číslo 15-16; s. 2619 - 2640
Hlavní autoři:	Bhardwaj, Akanksha, Konar, Partha, Ngairangbam, Vishal
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024 Springer Nature B.V Springer Science + Business Media
Témata:	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
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 SC 0016013 USDOE
ISSN:	1951-6355 1951-6401
DOI:	10.1140/epjs/s11734-024-01306-z