ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks Article Swipe
S. Chekanov
,
W. Islam
,
Rui Zhang
,
Nicholas Luongo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.03065
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.03065
A web-based tool called ADFilter was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small fraction of either collision data or Standard Model Monte Carlo simulations. The tool calculates loss distributions for input events, helping to determine the degree to which the events can be considered anomalous. It also calculates two-body invariant masses both before and after the autoencoders, as well as cross sections. Real-life examples are provided to demonstrate how the tool can be used to reinterpret existing LHC results with the goal of significantly improving exclusion limits.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.03065
- https://arxiv.org/pdf/2409.03065
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403555654
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403555654Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.03065Digital Object Identifier
- Title
-
ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-09-04Full publication date if available
- Authors
-
S. Chekanov, W. Islam, Rui Zhang, Nicholas LuongoList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.03065Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2409.03065Direct 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/2409.03065Direct OA link when available
- Concepts
-
Autoencoder, Anomaly detection, Anomaly (physics), Artificial neural network, Artificial intelligence, Computer science, Deep neural networks, Pattern recognition (psychology), Physics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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