{"ID":2842393,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10540","arxiv_id":"2511.10540","title":"Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers","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\u0026D for future collider detectors as well as hardware-based ML for edge applications in high energy physics.","short_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...","url_abs":"https://arxiv.org/abs/2511.10540","url_pdf":"https://arxiv.org/pdf/2511.10540v2","authors":"[\"Deniz Yilmaz\",\"Liangyu Wu\",\"Julia Gonski\",\"Dylan Rankin\",\"Christian Herwig\"]","published":"2025-11-13T17:39:22Z","proceeding":"physics.ins-det","tasks":"[\"physics.ins-det\",\"cs.LG\"]","methods":"[]","has_code":false}