Machine Learning Enables Real-Time Waveform Decomposition for Dual-Readout Calorimetry
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918472849555456 |
|---|---|
| author | Wu, Liangyu Liu, Qibin Lucchini, Marco Toliman Gonski, Julia Campajola, Marcello Moneta, Stefano |
| author_facet | Wu, Liangyu Liu, Qibin Lucchini, Marco Toliman Gonski, Julia Campajola, Marcello Moneta, Stefano |
| contents | Dual-readout calorimeters achieve superior energy resolution by simultaneously measuring Cherenkov and scintillation signals for event-by-event electromagnetic fraction correction, making them attractive for next-generation Higgs factories. However, if a full waveform readout is required for time-based analysis to separate Cherenkov and scintillation signals, high off-detector data rates might present challenges. These challenges can be mitigated by real-time signal processing in front-end electronics. We present a systematic comparison of machine learning (ML) and template fitting approaches for the separation of scintillation and Cherenkov light components in homogeneous dual-readout calorimeters across three representative crystal types. ML models achieve comparable signal extraction performance at lower sampling rates than template fitting. A single model trained over a range of incident particle energies demonstrates robust performance, and FPGA-compatible compression achieves latencies suitable for real-time application. This work establishes both baseline template fitting performance and ML-enhanced alternatives for crystal-based dual-readout calorimeters, offering practical pathways towards front-end feature extraction in future detector design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26090 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning Enables Real-Time Waveform Decomposition for Dual-Readout Calorimetry Wu, Liangyu Liu, Qibin Lucchini, Marco Toliman Gonski, Julia Campajola, Marcello Moneta, Stefano Instrumentation and Detectors High Energy Physics - Experiment Dual-readout calorimeters achieve superior energy resolution by simultaneously measuring Cherenkov and scintillation signals for event-by-event electromagnetic fraction correction, making them attractive for next-generation Higgs factories. However, if a full waveform readout is required for time-based analysis to separate Cherenkov and scintillation signals, high off-detector data rates might present challenges. These challenges can be mitigated by real-time signal processing in front-end electronics. We present a systematic comparison of machine learning (ML) and template fitting approaches for the separation of scintillation and Cherenkov light components in homogeneous dual-readout calorimeters across three representative crystal types. ML models achieve comparable signal extraction performance at lower sampling rates than template fitting. A single model trained over a range of incident particle energies demonstrates robust performance, and FPGA-compatible compression achieves latencies suitable for real-time application. This work establishes both baseline template fitting performance and ML-enhanced alternatives for crystal-based dual-readout calorimeters, offering practical pathways towards front-end feature extraction in future detector design. |
| title | Machine Learning Enables Real-Time Waveform Decomposition for Dual-Readout Calorimetry |
| topic | Instrumentation and Detectors High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2604.26090 |