Refining fast simulation using machine learning Article Swipe
Samuel Bein
,
Patrick Connor
,
K. Pedro
,
P. Schleper
,
M. Wolf
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.12919
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.12919
At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2. The FastSim chain is roughly 10 times faster than the application based on the GEANT4 detector simulation and full reconstruction referred to as FullSim. However, this advantage comes at the price of decreased accuracy in some of the final analysis observables. In this contribution, a machine learning-based technique to refine those observables is presented. We employ a regression neural network trained with a sophisticated combination of multiple loss functions to provide post-hoc corrections to samples produced by the FastSim chain. The results show considerably improved agreement with the FullSim output and an improvement in correlations among output observables and external parameters. This technique is a promising replacement for existing correction factors, providing higher accuracy and thus contributing to the wider usage of FastSim.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.12919
- https://arxiv.org/pdf/2309.12919
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387031677
All OpenAlex metadata
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https://openalex.org/W4387031677Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.12919Digital Object Identifier
- Title
-
Refining fast simulation using machine learningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-22Full publication date if available
- Authors
-
Samuel Bein, Patrick Connor, K. Pedro, P. Schleper, M. WolfList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.12919Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.12919Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2309.12919Direct OA link when available
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Granularity, Observable, Computer science, Monte Carlo method, Detector, Artificial neural network, Refining (metallurgy), Regression, Artificial intelligence, Algorithm, Statistics, Mathematics, Physics, Telecommunications, Chemistry, Operating system, Quantum mechanics, Physical chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | Cornell University |
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| publication_date | 2023-09-22 |
| publication_year | 2023 |
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