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Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion

by Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson

Submission summary

Authors (as registered SciPost users): Michael James Fenton · Kevin Greif
Submission information
Preprint Link: https://arxiv.org/abs/2404.14332v2  (pdf)
Code repository: https://github.com/Alexanders101/LVD
Data repository: https://zenodo.org/records/13364827
Date submitted: 2024-10-31 15:41
Submitted by: Greif, Kevin
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
Specialties:
  • High-Energy Physics - Experiment
  • High-Energy Physics - Phenomenology
Approaches: Experimental, Computational

Abstract

The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic top quark pair production at the Large Hadron Collider.

Author indications on fulfilling journal expectations

  • Provide a novel and synergetic link between different research areas.
  • Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
  • Detail a groundbreaking theoretical/experimental/computational discovery
  • Present a breakthrough on a previously-identified and long-standing research stumbling block

Author comments upon resubmission

Dear editors and reviewers,

Apologies for the delay in re-submission. Our lead author was away on a summer internship and has only recently returned. We have already responded to the reviewers in the comments section below. These comments refer to version 2 of the paper, now on the arXiv. We hope the new version addresses all of the reviewers concerns.

Sincerely,
Kevin for the team

List of changes

1. Section 4.2, 2nd paragraph: Addition of discussion of the ability to sample a single detector-level event multiple times.
2. Section 4.2, 7th paragraph: Discussion of corner plots presented in Appendix E
3. Section 6, 2nd paragraph: Remove sentence “This lack of prior dependence strongly motivates the use of VLD for unfolding”.
4. Section 6, 5th paragraph: Add statements on data and code availability.
5. Appendix B: Add definitions of the distance metrics used.
6. Appendix E: Add corner plots.

Current status:
In refereeing

Reports on this Submission

Report #1 by Anonymous (Referee 1) on 2024-12-3 (Invited Report)

Report

The reply of the authors has addressed all comments I made before. I recommend the improved manuscript for publication in SciPost Physics.

Requested changes

There is just one minor thing I noticed that the authors might want to adjust: Towards the end of section 4.1, the variable $M$ does not refer to the previously used multiplicity at detector level, but rather the mass. Maybe they could use $m$ instead?

Recommendation

Publish (easily meets expectations and criteria for this Journal; among top 50%)

  • validity: high
  • significance: top
  • originality: top
  • clarity: top
  • formatting: perfect
  • grammar: perfect

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