Data and Code for "Understanding Energy Dependent Hadronic Calorimeter Response from a Machine Learning Perspective"

This dataset supports the research presented in the paper "Understanding Energy Dependent Hadronic Calorimeter Response from a Machine Learning Perspective." It contains simulation data and machine learning scripts for hadronic energy reconstruction in dual-readout and high-granularity calorimeters.The dataset includes processed hadronic shower signals from 3 channels, including scintillation photon, Cherenkov photon, and charged particles.

The provided code demonstrates how to utilize these multi-channel signals to enhance energy resolution. These resources are intended to facilitate the development of advanced reconstruction algorithms for future high-energy physics experiments.