Anomaly detection with flow-based fast calorimeter simulators
Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using caloflow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost. As a demonstrati...
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| Main Authors: | , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
29 August 2024
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| In: |
Physical review
Year: 2024, Volume: 110, Issue: 3, Pages: 1-13 |
| ISSN: | 2470-0029 |
| DOI: | 10.1103/PhysRevD.110.035036 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1103/PhysRevD.110.035036 Verlag, kostenfrei, Volltext: https://link.aps.org/doi/10.1103/PhysRevD.110.035036 
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| Author Notes: | Claudius Krause, Benjamin Nachman, Ian Pang, David Shih, and Yunhao Zhu |
| Summary: | Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using caloflow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost. As a demonstration, we consider electromagnetic showers initiated by one (background) or multiple (signal) photons. The caloflow model is designed to generate single-photon showers, but it also provides access to the shower likelihood. We use this likelihood as an anomaly score and study the showers tagged as being unlikely. As expected, the tagger struggles when the signal photons are nearly collinear but is otherwise effective. This approach is complementary to a supervised classifier trained on only specific signal models using the same low-level calorimeter inputs. While the supervised classifier is also highly effective at unseen signal models, the unsupervised method is more sensitive in certain regions, and, thus, we expect that the ultimate performance will require a combination of these approaches. |
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| Item Description: | Gesehen am 05.11.2024 |
| Physical Description: | Online Resource |
| ISSN: | 2470-0029 |
| DOI: | 10.1103/PhysRevD.110.035036 |