Recognition: unknown
Machine Learning Enables Real-Time Waveform Decomposition for Dual-Readout Calorimetry
Pith reviewed 2026-05-07 12:17 UTC · model grok-4.3
The pith
Machine learning achieves comparable waveform decomposition performance to template fitting for dual-readout crystals at reduced sampling rates and supports FPGA implementation for real-time use.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
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.
Load-bearing premise
That performance measured on the tested crystal types and simulated or controlled conditions will hold under full experimental conditions with real detector noise, calibration variations, and integration into a complete readout chain.
read the original abstract
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.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript systematically compares machine learning (ML) and template fitting methods for decomposing waveforms into scintillation and Cherenkov light components in homogeneous dual-readout calorimeters for three crystal types. It reports that ML models achieve comparable signal extraction performance at lower sampling rates, with a single model showing robust performance across incident particle energies, and that FPGA-compatible model compression enables latencies suitable for real-time front-end processing.
Significance. This work is significant in providing practical pathways for real-time feature extraction in dual-readout calorimeters, which are promising for next-generation Higgs factory detectors due to their superior energy resolution via event-by-event electromagnetic fraction correction. The establishment of baseline template fitting performance and exploration of ML alternatives could help mitigate high data rate challenges. Strengths include the empirical comparison across crystal types and the focus on FPGA compatibility for deployment.
major comments (2)
- [Results] The abstract and results claim comparable performance without supplying quantitative metrics, error bars, training/test split details, or hyperparameter handling information, which is necessary to evaluate the robustness of the ML approach relative to template fitting.
- [Methods and Validation] Performance is demonstrated on waveforms generated from templates or controlled lab setups for three crystal types, but the manuscript does not test or discuss performance under real detector conditions including noise spectra, gain variations, temperature effects, and full signal chain integration; this assumption is load-bearing for the claimed sampling-rate advantage and real-time suitability.
minor comments (1)
- [Abstract] Consider adding specific quantitative results (e.g., resolution values or latency numbers) to the abstract for better context.
Simulated Author's Rebuttal
We thank the referee for their constructive and detailed review. We address each major comment below and describe the revisions we will make to strengthen the manuscript.
read point-by-point responses
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Referee: [Results] The abstract and results claim comparable performance without supplying quantitative metrics, error bars, training/test split details, or hyperparameter handling information, which is necessary to evaluate the robustness of the ML approach relative to template fitting.
Authors: We agree that additional quantitative details are needed for a rigorous evaluation. In the revised manuscript we will report explicit performance metrics (e.g., RMSE and correlation coefficients for scintillation and Cherenkov amplitudes) together with error bars obtained from repeated training runs with different random seeds. We will also specify the training/validation/test split ratios, the cross-validation procedure used, and the hyperparameter search ranges and final values for each model architecture. These additions will appear in a new subsection of the methods and in the results figures and tables. revision: yes
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Referee: [Methods and Validation] Performance is demonstrated on waveforms generated from templates or controlled lab setups for three crystal types, but the manuscript does not test or discuss performance under real detector conditions including noise spectra, gain variations, temperature effects, and full signal chain integration; this assumption is load-bearing for the claimed sampling-rate advantage and real-time suitability.
Authors: We acknowledge that the present study is limited to controlled template-based and laboratory waveforms, as stated in the methods. Full validation under realistic detector conditions (noise spectra, gain drifts, temperature variations, and complete front-end electronics) is not feasible with the current data set and would require access to an integrated detector prototype that is not yet available. In the revision we will add an explicit limitations paragraph that quantifies the expected impact of these effects using published noise and temperature coefficients for the crystals, and we will outline a concrete plan for future beam-test validation. The relative advantage of the ML models at reduced sampling rates is demonstrated under the same controlled conditions used for the template-fitting baseline, so the comparative claim remains valid, but we will clearly state that absolute performance in a full detector environment remains to be verified. revision: partial
Circularity Check
No circularity: empirical ML-template comparison is self-contained
full rationale
The paper reports direct empirical benchmarks of ML waveform decomposition versus template fitting on three crystal types, using controlled or simulated waveforms. Performance is measured via standard metrics (signal extraction accuracy, sampling-rate dependence, latency) on held-out test data; no equations, fitted parameters, or self-citations are invoked to derive the headline results. The central claims therefore do not reduce to inputs by construction and remain falsifiable against external detector data.
discussion (0)
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