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

• URL: http://arxiv.org/abs/2511.10540v2
• Date: Sun, 16 Nov 2025 17:00:27 GMT
• Title: Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers
• Authors: Deniz Yilmaz, Liangyu Wu, Julia Gonski, Dylan Rankin, Christian Herwig,
• Abstract summary: Machine learning at the "edge", or in cell-level readout, can dramatically reduce the off-detector data rate for high-granularity drift chambers.<n>We present machine learning algorithms for cluster counting in real-time readout of future drift chambers.
• Score: 0.028193358315036943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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