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/2105.04582v1 (pdf) |
| Date submitted: | 2022-01-24 21:17 |
| 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.
Current status:
Reports on this Submission
Report 1 by Tilman Plehn on 2022-8-15 (Invited Report)
Report
The paper asks a very interesting question and it uses state-of-the-art tools in a new way. So it should be published. However, I am unsure about the presentation and the story the paper tells right now. The goal seems to be to measure the quantum numbers of a hadronically resonance, the answer seems to be that we can tell decays to quarks from decays to gluons?