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May 8 – 10, 2023
University of Pittsburgh
US/Eastern timezone

Decision tree autoencoder anomaly detection on FPGA at L1 triggers

May 8, 2023, 5:30 PM
15m
Lawrence Hall 203

Lawrence Hall 203

Speaker

Stephen Roche (Saint Louis University)

Description

We present a decision tree-based implementation of autoencoder anomaly detection. A novel algorithm is presented in which a forest of decision trees is trained only on background and used as an anomaly detector. The fwX platform is used to deploy the trained autoencoder on FPGAs within the latency and resource constraints demanded by level 1 trigger systems. Results are presented with two datasets: a BSM Higgs decay to pseudoscalars with a 4l final state, and the LHC physics dataset for unsupervised New Physics detection. Finally, the effects of signal contamination on the training set are presented, demonstrating the possibility of training on data.

This work is detailed in 2304.03836.

Primary author

Stephen Roche (Saint Louis University)

Co-authors

Ben Carlson (Westmont College) Tae Min Hong (University of Pittsburgh (US))

Presentation materials