SciPost Submission Page
Identifying the Quantum Properties of Hadronic Resonances using Machine Learning
by Jakub Filipek, Shih-Chieh Hsu, John Kruper, Kirtimaan Mohan, Benjamin Nachman
Submission summary
| Authors (as registered SciPost users): | Kirtimaan Mohan |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2105.04582v2 (pdf) |
| Date submitted: | 2024-12-05 18:43 |
| Submitted by: | Mohan, Kirtimaan |
| Submitted to: | SciPost Physics Core |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
Abstract
With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also be used to identify its quantum numbers. Convolutional neural networks (CNNs) using jet-images can significantly improve upon existing techniques to identify the quantum chromodynamic (QCD) (`color') as well as the spin of a two-prong resonance using its substructure. Additionally, jet-images are useful in determining what information in the jet radiation pattern is useful for classification, which could inspire future taggers. These techniques improve the categorization of new particles and are an important addition to the growing jet substructure toolkit, for searches and measurements at the LHC now and in the future.
Author comments upon resubmission
List of changes
Expanded on introduction and conclusion, clarifying in the text how we envision this classifier is used in practice as well as its limitations.
Current status:
Reports on this Submission
Report #1 by Tilman Plehn (Referee 1) on 2024-12-26 (Invited Report)
Report
Thank you to the authors for considering by questions and comments. I am happy now. Let's move fast and publish the paper, I have no idea where it got stuck, but I think we can sort thing this out easily...
Recommendation
Publish (easily meets expectations and criteria for this Journal; among top 50%)