Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers

TL;DR

This paper develops machine learning algorithms for real-time cluster counting in high-granularity drift chambers, reducing data rates and improving particle identification for future collider detectors, with FPGA implementation feasibility.

Contribution

It introduces novel ML algorithms optimized for FPGA deployment to perform cluster counting at the detector source, surpassing traditional methods in accuracy and speed.

Findings

ML algorithms outperform derivative-based techniques in pion-kaon separation

FPGA implementation achieves real-time latency suitable for future collider scenarios

Demonstrates feasibility of edge ML for high-energy physics detector readout

Abstract

Drift chambers have long been central to collider tracking, but future machines like a Higgs factory motivate higher granularity and cluster counting for particle ID, posing new data processing challenges. Machine learning (ML) at the "edge", or in cell-level readout, can dramatically reduce the off-detector data rate for high-granularity drift chambers by performing cluster counting at-source. We present machine learning algorithms for cluster counting in real-time readout of future drift chambers. These algorithms outperform traditional derivative-based techniques based on achievable pion-kaon separation. When synthesized to FPGA resources, they can achieve latencies consistent with real-time operation in a future Higgs factory scenario, thus advancing both R&D for future collider detectors as well as hardware-based ML for edge applications in high energy physics.