Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors
Detectors in next-generation high-energy physics experiments face several daunting requirements, such as high data rates, damaging radiation exposure, and stringent constraints on power, space, and latency. To address these challenges, machine learning in readout electronics can be leveraged for sma...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adf0c0 |
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| _version_ | 1849245688616976384 |
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| author | Alexander Yue Haoyi Jia Julia Gonski |
| author_facet | Alexander Yue Haoyi Jia Julia Gonski |
| author_sort | Alexander Yue |
| collection | DOAJ |
| description | Detectors in next-generation high-energy physics experiments face several daunting requirements, such as high data rates, damaging radiation exposure, and stringent constraints on power, space, and latency. To address these challenges, machine learning in readout electronics can be leveraged for smart detector designs, enabling intelligent inference and data reduction at-source. Variational autoencoders (VAEs) offer a variety of benefits for front-end readout; an on-sensor encoder can perform efficient lossy data compression while simultaneously providing a latent space representation that can be used for anomaly detection. Results are presented from low-latency and resource-efficient VAEs for front-end data processing in a futuristic silicon pixel detector. Encoder-based data compression is found to preserve good performance of off-detector analysis while significantly reducing the off-detector data rate as compared to a similarly sized data filtering approach. Furthermore, the latent space information is found to be a useful discriminator in the context of real-time sensor defect monitoring. Together, these results highlight the multifaceted utility of autoencoder-based front-end readout schemes and motivate their consideration in future detector designs. |
| format | Article |
| id | doaj-art-b5f8e0d72057446ab0fbc6abaee9c8b4 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-b5f8e0d72057446ab0fbc6abaee9c8b42025-08-20T03:58:44ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303501710.1088/2632-2153/adf0c0Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectorsAlexander Yue0https://orcid.org/0000-0001-9901-9649Haoyi Jia1https://orcid.org/0000-0001-9191-3822Julia Gonski2https://orcid.org/0000-0003-2037-6315Stanford University , 450 Jane Stanford Way, Stanford, CA 94305, United States of AmericaStanford University , 450 Jane Stanford Way, Stanford, CA 94305, United States of America; SLAC National Accelerator Laboratory , 2575 Sand Hill Road MS 95, Menlo Park, CA 94025, United States of AmericaSLAC National Accelerator Laboratory , 2575 Sand Hill Road MS 95, Menlo Park, CA 94025, United States of AmericaDetectors in next-generation high-energy physics experiments face several daunting requirements, such as high data rates, damaging radiation exposure, and stringent constraints on power, space, and latency. To address these challenges, machine learning in readout electronics can be leveraged for smart detector designs, enabling intelligent inference and data reduction at-source. Variational autoencoders (VAEs) offer a variety of benefits for front-end readout; an on-sensor encoder can perform efficient lossy data compression while simultaneously providing a latent space representation that can be used for anomaly detection. Results are presented from low-latency and resource-efficient VAEs for front-end data processing in a futuristic silicon pixel detector. Encoder-based data compression is found to preserve good performance of off-detector analysis while significantly reducing the off-detector data rate as compared to a similarly sized data filtering approach. Furthermore, the latent space information is found to be a useful discriminator in the context of real-time sensor defect monitoring. Together, these results highlight the multifaceted utility of autoencoder-based front-end readout schemes and motivate their consideration in future detector designs.https://doi.org/10.1088/2632-2153/adf0c0machine learningedgeautoencodercolliderreadout electronics |
| spellingShingle | Alexander Yue Haoyi Jia Julia Gonski Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors Machine Learning: Science and Technology machine learning edge autoencoder collider readout electronics |
| title | Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors |
| title_full | Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors |
| title_fullStr | Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors |
| title_full_unstemmed | Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors |
| title_short | Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors |
| title_sort | variational autoencoders for at source data reduction and anomaly detection in high energy particle detectors |
| topic | machine learning edge autoencoder collider readout electronics |
| url | https://doi.org/10.1088/2632-2153/adf0c0 |
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