https://doi.org/10.1140/epjs/s11734-024-01306-z
Review
Foundations of automatic feature extraction at LHC–point clouds and graphs
1
Department of Physics, Oklahoma State University, 74078, Stillwater, OH, USA
2
Theoretical Physics Division, Physical Research Laboratory, Shree Pannalal Patel Marg, 380009, Ahmedabad, Gujarat, India
3
Institute for Particle Physics Phenomenology, Department of Physics, Durham University, South Road, DH1 3LE, Durham, UK
c
vishal.s.ngairangbam@durham.ac.uk
Received:
26
April
2024
Accepted:
23
August
2024
Published online:
11
September
2024
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.
© The Author(s) 2024
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