Understanding Energy Dependent Hadronic Calorimeter Response from a Machine Learning Perspective
To meet the precision requirements of future high-energy physics experiments, improving the energy resolution of hadronic calorimeters remains a critical challenge. This work presents a systematic investigation of hadronic energy reconstruction using machine learning, highlighting the roles of various signal channels, including scintillation light, Cherenkov light, charged particles, and the full three-dimensional topology of hadronic showers in the energy range up to 10 GeV. Throughout this study, detector effects are not taken into account. Under these conditions, the intrinsic resolution of hadronic showers reaches approximately $(10.8\pm0.3)\% / \sqrt{E/GeV}$ when all signal channels and the full 3D shower information are fully utilized. Compared with the traditional signal-summing approach, machine-learning-based reconstruction can significantly improve energy resolution, even under a limited sampling fraction of 10\%, enhancing it from $(57.6\pm3.7)\%/\sqrt{E/GeV}$ to $(34.1\pm2.8)\%/\sqrt{E/GeV}$. These results highlight the critical importance of both multi-channel information and detailed spatial shower features in hadronic energy reconstruction, and demonstrate the substantial potential of combining high-granularity and dual-readout calorimeter designs with machine-learning-based reconstruction techniques for future experiments.