SciPost Submission Page
Encoding off-shell effects in top pair production in Direct Diffusion networks
by Mathias Kuschick
Submission summary
| Authors (as registered SciPost users): | Mathias Kuschick |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2412.17783v3 (pdf) |
| Date accepted: | 2025-03-25 |
| Date submitted: | 2025-03-14 12:44 |
| Submitted by: | Kuschick, Mathias |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 17th International Workshop on Top Quark Physics (TOP2024) |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
Abstract
To meet the precision targets of upcoming LHC runs in the simulation of top pair production events it is essential to also consider off-shell effects. Due to their great computational cost I propose to encode them in neural networks. For that I use a combination of neural networks that take events with approximate off-shell effects and transform them into events that match those obtained with full off-shell calculations. This was shown to work reliably and efficiently at leading order. Here I discuss first steps extending this method to include higher order effects.
List of changes
- a paragraph on Schrödinger bridges was added
- a paragraph further describing the changes in the sample distributions in comparison to the original study was added
- I added the information that the classifier is not yet bayesianized
Current status:
Editorial decision:
For Journal SciPost Physics Proceedings: Publish
(status: Editorial decision fixed and (if required) accepted by authors)
Reports on this Submission
Report #1 by Tilman Plehn (Referee 1) on 2025-3-18 (Invited Report)
Report
Thank you for considering my comments, I am happy.
Recommendation
Publish (surpasses expectations and criteria for this Journal; among top 10%)