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Hybrid tree-based machine learning for calorimetric energy calibration using the Lorenzetti Showers framework
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DOI：10.1016/j.measurement.2026.122771.png)
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Abstract
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• Machine-learning-based calibration improved fast energy measurement within the considered Lorenzetti simulation benchmark. • Hybrid tree-based machine-learning models achieved the best calibration performance. • Compact latent-space models retained competitive accuracy while reducing detector-signal dimensionality. • The proposed methods improved RMSE, IQR, energy resolution, and threshold-response behavior. • Top-ranked configurations showed low CPU-based prediction times for the trained-model inference stage.
Keywords:
Energy measurement
Particle detectors
Data-driven calibration
Machine learning
Calorimetric instrumentation
Computational efficiency
Lorenzetti showers framework
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