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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.10540 |
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| _version_ | 1866912711991885824 |
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| author | Yilmaz, Deniz Wu, Liangyu Gonski, Julia Rankin, Dylan Herwig, Christian |
| author_facet | Yilmaz, Deniz Wu, Liangyu Gonski, Julia Rankin, Dylan Herwig, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10540 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers Yilmaz, Deniz Wu, Liangyu Gonski, Julia Rankin, Dylan Herwig, Christian Instrumentation and Detectors Machine Learning 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. |
| title | Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers |
| topic | Instrumentation and Detectors Machine Learning |
| url | https://arxiv.org/abs/2511.10540 |