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Lund jet plane for Higgs tagging

by Charanjit K. Khosa

This is not the latest submitted version.

Submission summary

As Contributors: Charanjit Kaur Khosa
Preprint link: scipost_202112_00041v1
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)
Academic field: Physics
  • High-Energy Physics - Phenomenology
Approaches: Computational, Phenomenological


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.

Current status:
Has been resubmitted

Submission & Refereeing History

Resubmission 2110.15135v2 on 19 January 2022

Reports on this Submission

Anonymous Report 1 on 2022-1-15 (Invited 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.

  • validity: -
  • significance: -
  • originality: -
  • clarity: -
  • formatting: -
  • grammar: -

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!

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