Triggerless data acquisition pipeline for Machine Learning based statistical anomaly detection

2023/11/03 by Grosso, Gaia, Lai, Nicolò, Migliorini, Matteo +4
#FOS: Physical sciences #High Energy Physics - Experiment (hep-ex)

paper · doi:10.48550/arxiv.2311.02038

Abstract

This work describes an online processing pipeline designed to identify anomalies in a continuous stream of data collected without external triggers from a particle detector. The processing pipeline begins with a local reconstruction algorithm, employing neural networks on an FPGA as its first stage. Subsequent data preparation and anomaly detection stages are accelerated using GPGPUs. As a practical demonstration of anomaly detection, we have developed a data quality monitoring application using a cosmic muon detector. Its primary objective is to detect deviations from the expected operational conditions of the detector. This serves as a proof-of-concept for a system that can be adapted for use in large particle physics experiments, enabling anomaly detection on datasets with reduced bias.

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