Matches in SemOpenAlex for { <https://semopenalex.org/work/W3191500321> ?p ?o ?g. }
- W3191500321 abstract "In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely challenging environment of a real-time event selection system at the Large Hadron Collider (LHC). We demonstrate that new physics signatures can be enhanced by three orders of magnitude, while staying within the strict latency and resource constraints of a typical LHC event filtering system. This would allow for collecting high-statistics, potentially new physics enhanced datasets for novel physics searches. Through per-layer, highly parallel implementations of network layers, support for autoencoder-specific losses on FPGAs and latent space based inference, we demonstrate that anomaly detection can be performed as fast as $80,$ns using less than 3% of FPGA resources." @default.
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- W3191500321 date "2021-08-10" @default.
- W3191500321 modified "2023-10-12" @default.
- W3191500321 title "Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider" @default.
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