Analysis-Specific Fast Simulation at the LHC with Deep Learning

Chen, C. and Cerri, O. and Nguyen, T. Q. and Vlimant, J. R. and Pierini, M. (2021) Analysis-Specific Fast Simulation at the LHC with Deep Learning. Computing and Software for Big Science, 5 . Art. No. 15. ISSN 2510-2036. PMCID PMC8549944. doi:10.1007/s41781-021-00060-4. https://resolver.caltech.edu/CaltechAUTHORS:20210622-201201338

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Abstract

We present a fast-simulation application based on a deep neural network, designed to create large analysis-specific datasets. Taking as an example the generation of W + jet events produced in √s = 13 TeV proton–proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. The adoption of this approach would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow. This strategy could help the high energy physics community to face the computing challenges of the future High-Luminosity LHC.

Item Type:

Article

Related URLs:

URL	URL Type	Description
https://doi.org/10.1007/s41781-021-00060-4	DOI	Article
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8549944	PubMed Central	Article
https://arxiv.org/abs/2010.01835	arXiv	Discussion Paper

ORCID:

Author	ORCID
Cerri, O.	0000-0002-2191-0666
Nguyen, T. Q.	0000-0003-3954-5131
Vlimant, J. R.	0000-0002-9705-101X
Pierini, M.	0000-0003-1939-4268

Alternate Title:

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

Additional Information:

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Received 12 October 2020; Accepted 12 May 2021; Published 09 June 2021. This project is partially supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement no 772369) and by the United States Department of Energy, Office of High Energy Physics Research under Caltech Contract No. DE-SC0011925. This work was conducted at “iBanks,” the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of “iBanks.” Open Access funding provided by CERN. On behalf of all authors, the corresponding author states that there is no conflict of interest.

Group:

CMS@Caltech

Funders:

Funding Agency	Grant Number
European Research Council (ERC)	772369
Department of Energy (DOE)	DE-SC0011925
NVIDIA Corporation	UNSPECIFIED
SuperMicro Corporation	UNSPECIFIED
Kavli Foundation	UNSPECIFIED
CERN	UNSPECIFIED

Subject Keywords:

Hadron Collider Physics; Fast Simulation; Deep Learning; High Energy Physics computing

PubMed Central ID:

PMC8549944

DOI:

10.1007/s41781-021-00060-4

Record Number:

CaltechAUTHORS:20210622-201201338

Persistent URL:

https://resolver.caltech.edu/CaltechAUTHORS:20210622-201201338

Official Citation:

Chen, C., Cerri, O., Nguyen, T.Q. et al. Analysis-Specific Fast Simulation at the LHC with Deep Learning. Comput Softw Big Sci 5, 15 (2021). https://doi.org/10.1007/s41781-021-00060-4

Usage Policy:

No commercial reproduction, distribution, display or performance rights in this work are provided.

ID Code:

109533

Collection:

CaltechAUTHORS

Deposited By:

Tony Diaz

Deposited On:

23 Jun 2021 19:12

Last Modified:

02 Nov 2021 16:26

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