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Kicking it Off(-shell) with Direct Diffusion
by Anja Butter, Tomas Jezo, Michael Klasen, Mathias Kuschick, Sofia Palacios Schweitzer, Tilman Plehn
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Tomas Jezo · Sofia Palacios Schweitzer · Tilman Plehn |
| Submission information | |
|---|---|
| Preprint Link: | scipost_202404_00006v2 (pdf) |
| Date accepted: | 2024-08-22 |
| Date submitted: | 2024-07-15 15:41 |
| Submitted by: | Palacios Schweitzer, Sofia |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
Abstract
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We present a novel method to transform high-dimensional distributions based on a diffusion neural network and use it to generate a process with off-shell kinematics from the much simpler on-shell one. Applied to a toy example of top pair production at LO we show how our method generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.
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 Editor,
Thank you so much for carefully considering the input from all the referees and determining the best next steps. We are also grateful for your valuable suggestions on how to improve our manuscript and to address the concerns raised by Referee No. 2. Please find replies/remarks to all of your points threaded into your letter.
Sincerely, The authors
Would it be possible to address the concerns of Referee No. 2 by discussing this point more explicitly and extensively in your paper, including emphasizing in the paper how your study is mainly about a novel application of ML ideas that is different from what done so far, We modified the abstract to make the goal of our study clear from the outset. Moreover, we fine tuned the introduction such that the emphasis on the novel ML idea is increased. and possibly also listing the extension to NLO QCD as part of the future developments? It would be helpful to maybe also add insights on what you expect to be the challenges involved and how you would plan to address them. We added a paragraph in the Outlook describing extensions to higher orders and the possible issues that may arise and how we’d address them.
Published as SciPost Phys. Core 7, 064 (2024)
Reports on this Submission
Report
The corrections made to the article go in the right direction and clarify the essence of the current study, while not exaggerating that the method used in the article can generate off-shell effects at higher-orders without any further modifications/problems. I can recommend the article for publication in its current form. However, I would like to encourage the authors to repeat the study at least at the NLO level in QCD.
Recommendation
Publish (meets expectations and criteria for this Journal)