Classifier surrogates: sharing AI-based searches with the world
Abstract In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely on experiment-internal data as well as full detecto...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-09-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-024-13353-w |
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| _version_ | 1846164886014394368 |
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| author | Sebastian Bieringer Gregor Kasieczka Jan Kieseler Mathias Trabs |
| author_facet | Sebastian Bieringer Gregor Kasieczka Jan Kieseler Mathias Trabs |
| author_sort | Sebastian Bieringer |
| collection | DOAJ |
| description | Abstract In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely on experiment-internal data as well as full detector simulations. We show a concrete implementation of a newly proposed strategy, so-called Classifier Surrogates, to be trained inside the experiments, that only utilise publicly accessible features and truth information. These surrogates approximate the original classifier distribution, and can be shared with the public. Subsequently, such a model can be evaluated by sampling the classification output from high-level information without requiring a sophisticated detector simulation. Technically, we show that continuous normalizing flows are a suitable generative architecture that can be efficiently trained to sample classification results using conditional flow matching. We further demonstrate that these models can be easily extended by Bayesian uncertainties to indicate their degree of validity when confronted with unknown inputs by the user. For a concrete example of tagging jets from hadronically decaying top quarks, we demonstrate the application of flows in combination with uncertainty estimation through either inference of a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights. |
| format | Article |
| id | doaj-art-252ff79fd37144de9e72d5e535a4fcbd |
| institution | Kabale University |
| issn | 1434-6052 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj-art-252ff79fd37144de9e72d5e535a4fcbd2024-11-17T12:45:22ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522024-09-0184911010.1140/epjc/s10052-024-13353-wClassifier surrogates: sharing AI-based searches with the worldSebastian Bieringer0Gregor Kasieczka1Jan Kieseler2Mathias Trabs3Institut für Experimentalphysik, Universität HamburgInstitut für Experimentalphysik, Universität HamburgInstitut für Experimentelle Teilchenphysik, Karlsruher Institut für TechnologieInstitut für Stochastik, Karlsruher Institut für TechnologieAbstract In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely on experiment-internal data as well as full detector simulations. We show a concrete implementation of a newly proposed strategy, so-called Classifier Surrogates, to be trained inside the experiments, that only utilise publicly accessible features and truth information. These surrogates approximate the original classifier distribution, and can be shared with the public. Subsequently, such a model can be evaluated by sampling the classification output from high-level information without requiring a sophisticated detector simulation. Technically, we show that continuous normalizing flows are a suitable generative architecture that can be efficiently trained to sample classification results using conditional flow matching. We further demonstrate that these models can be easily extended by Bayesian uncertainties to indicate their degree of validity when confronted with unknown inputs by the user. For a concrete example of tagging jets from hadronically decaying top quarks, we demonstrate the application of flows in combination with uncertainty estimation through either inference of a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights.https://doi.org/10.1140/epjc/s10052-024-13353-w |
| spellingShingle | Sebastian Bieringer Gregor Kasieczka Jan Kieseler Mathias Trabs Classifier surrogates: sharing AI-based searches with the world European Physical Journal C: Particles and Fields |
| title | Classifier surrogates: sharing AI-based searches with the world |
| title_full | Classifier surrogates: sharing AI-based searches with the world |
| title_fullStr | Classifier surrogates: sharing AI-based searches with the world |
| title_full_unstemmed | Classifier surrogates: sharing AI-based searches with the world |
| title_short | Classifier surrogates: sharing AI-based searches with the world |
| title_sort | classifier surrogates sharing ai based searches with the world |
| url | https://doi.org/10.1140/epjc/s10052-024-13353-w |
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