The use of Generative Adversarial Networks to characterise new physics\n in multi-lepton final states at the LHC Article Swipe
Thabang Lebese
,
B. R. Mellado Garcia
,
X. Ruan
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2105.14933
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2105.14933
Semi-supervision in Machine Learning can be used in searches for new physics\nwhere the signal plus background regions are not labelled. This strongly\nreduces model dependency in the search for signals Beyond the Standard Model.\nThis approach displays the drawback in that over-fitting can give rise to fake\nsignals. Tossing toy Monte Carlo (MC) events can be used to estimate the\ncorresponding trials factor through a frequentist inference. However, MC events\nthat are based on full detector simulations are resource intensive. Generative\nAdversarial Networks (GANs) can be used to mimic MC generators. GANs are\npowerful generative models, but often suffer from training instability. We\nhenceforth show a review of GANs. We advocate the use of Wasserstein GAN (WGAN)\nwith weight clipping and WGAN with gradient penalty (WGAN-GP) where the norm of\ngradient of the critic is penalized with respect to its input. Following the\nemergence of multi-lepton anomalies, we apply GANs for the generation of\ndi-leptons final states in association with $b$-quarks at the LHC. A good\nagreement between the MC and the WGAN-GP generated events is found for the\nobservables selected in the study.\n
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Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/2105.14933
- https://arxiv.org/pdf/2105.14933
- OA Status
- green
- Cited By
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287162383
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4287162383Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2105.14933Digital Object Identifier
- Title
-
The use of Generative Adversarial Networks to characterise new physics\n in multi-lepton final states at the LHCWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2021Year of publication
- Publication date
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2021-05-31Full publication date if available
- Authors
-
Thabang Lebese, B. R. Mellado Garcia, X. RuanList of authors in order
- Landing page
-
https://arxiv.org/abs/2105.14933Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2105.14933Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2105.14933Direct OA link when available
- Concepts
-
Large Hadron Collider, Observable, Inference, Particle physics, Physics beyond the Standard Model, Frequentist inference, Computer science, Lepton, Physics, Generative grammar, Artificial intelligence, Nuclear physics, Bayesian inference, Electron, Bayesian probability, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 3, 2022: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.strongly\nreduces | 21 |
| abstract_inverted_index.the\ncorresponding | 56 |
| abstract_inverted_index.Generative\nAdversarial | 75 |
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| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
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| citation_normalized_percentile.is_in_top_10_percent | False |