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Lund jet plane for Higgs tagging
by Charanjit K. Khosa
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Submission summary
| Authors (as registered SciPost users): | Charanjit Kaur Khosa |
| Submission information | |
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| Preprint Link: | scipost_202112_00041v1 (pdf) |
| Date submitted: | 2021-12-18 22:22 |
| Submitted by: | Khosa, Charanjit Kaur |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | 50th International Symposium on Multiparticle Dynamics (ISMD2021) |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
Abstract
We study the boosted Higgs tagging using the Lund jet plane. The convolutional neural network is used for the Lund images data set to classify hadronically decaying Higgs from the QCD background. We consider $H\to b \bar{b}$ and $H \to gg$ decay for moderate and high Higgs transverse momentum and compare the performance with the cut based approach using the jet color ring observable. The approach using Lund plane images provides good tagging efficiency for all the cases.
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Reports on this Submission
Report
Nicely written proceeding, almost ready to go. Minor suggestions:
1. It would be good to cite the first (ATLAS) measurement of LJP ;-)
2. Please mention Pythia8 tune, results do depend on it.
3. we cluster the charge particles -> charged. Also since this is a particle level study, I would recommend using charged particles everywhere rather than tracks.
Author: Charanjit Kaur Khosa on 2022-01-19 [id 2107]
(in reply to Report 1 on 2022-01-15)I thank the referee for carefully reading the proceedings and for suggesting the important points. In the revised version, I have addressed all the points.
Anonymous on 2022-01-19 [id 2114]
(in reply to Charanjit Kaur Khosa on 2022-01-19 [id 2107])Thanks for addressing my comments so promptly. I believe this is ready to go!