SciPost Submission Page
How to GAN Event Unweighting
by Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder
- Published as SciPost Phys. 10, 089 (2021)
|As Contributors:||Tilman Plehn · Ramon Winterhalder|
|Arxiv Link:||https://arxiv.org/abs/2012.07873v3 (pdf)|
|Date submitted:||2021-03-25 10:44|
|Submitted by:||Winterhalder, Ramon|
|Submitted to:||SciPost Physics|
Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.
Published as SciPost Phys. 10, 089 (2021)
Submission & Refereeing History
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- Report 3 submitted on 2021-02-23 20:18 by Anonymous
- Report 2 submitted on 2021-02-22 17:17 by Anonymous
- Report 1 submitted on 2021-02-19 00:20 by Anonymous
Reports on this Submission
Anonymous Report 2 on 2021-4-7 Invited Report
- Cite as: Anonymous, Report on arXiv:2012.07873v3, delivered 2021-04-07, doi: 10.21468/SciPost.Report.2762
thanks for clarifying the jargon in the new version.
Did you have any thoughts on my previous question as to where that small horizontal shift between the uwGAN curve and true distribution in the bottom left/right plots of Fig1 might come from (also visible as a slope in the bottom left plot of Fig3)?
No specific changes requested.
Anonymous Report 1 on 2021-3-29 Invited Report
The authors have addressed my comments sufficiently. I am happy for publication to proceed.