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Machine Learning and LHC Event Generation
by Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Gonçalves, Eilam Gross, Theo Heimel, Gudrun Heinrich, Lukas Heinrich, Alexander Held, Stefan Höche, Jessica N. Howard, Philip Ilten, Joshua Isaacson, Timo Janßen, Stephen Jones, Marumi Kado, Michael Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kröninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, Daniel Maitre, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew Schwartz, David Shih, Frank Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, Daniel Whiteson, Ramon Winterhalder, Jure Zupan
This is not the latest submitted version.
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Stefan Höche · Joshua Isaacson · Timo Janßen · Sabine Kraml · Claudius Krause · Frank Krauss · Tilman Plehn · Steffen Schumann · Frank Siegert · Rob Verheyen · Ramon Winterhalder |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2203.07460v1 (pdf) |
| Date submitted: | 2022-04-06 20:59 |
| Submitted by: | Plehn, Tilman |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approach: | Phenomenological |
Abstract
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.
Current status:
Reports on this Submission
Anonymous Report 1 on 2022-10-12 (Invited Report)
- Cite as: Anonymous, Report on arXiv:2203.07460v1, delivered 2022-10-12, doi: 10.21468/SciPost.Report.5879
Report
The authors review recent progress in the application of artificial intelligence/machine learning to event generation at the LHC.
As far as I can tell, this submission is identical to the contribution of the same authors to the Snowmass process.
Since the authors submitted this review to SciPost Physics, and since it is a review rather than original work and does not meet any of the 4 'Expectations' outlined in the SciPost Physics criteria, I do not think this should be published here. This is only my opinion and I defer the final decision on this point to the editor in charge. The review certainly meets the 'General acceptance criteria'.
If the editor decides it is publishable, I would like the authors to address or implement the following:
1. remove the 'Executive summary ' section since it does not add value to a technical review.
2. The assertion in the last sentence of the first paragraph in the introduction seems too strong. Namely, the one that reads "... will help provide the simulations needed for the LHC...". Perhaps something softer like "... has the potential to provide..." is more fitting since, as far as I know, there are no production ready generative models at the moment (which are the subject of ref. [6] cited at the end of that sentence).
3. The toolbox of traditional tools for event generation for the LHC is very rich and diverse, yet the authors cite this literature very narrowly in refs. [1-5] . What is the the rationale behind this choice?
Author: Tilman Plehn on 2022-12-28 [id 3191]
(in reply to Report 1 on 2022-10-12)We would like to thank the referee and accommodated all his/her requests. For the non-ML aspects of event generators we added the corresponding Snowmass-inspired review as Ref.[6].