Calorimeter Shower Dataset and Diffusion Model Checkpoints for Gradient-Based Detector Optimization

This dataset accompanies the paper “Differentiable Surrogate for Detector Simulation and Design with Diffusion Models”, available at Machine Learning: Science and Technology (2026), DOI: https://doi.org/10.1088/2632-2153/ae5c56. This dataset contains preprocessed photon shower simulation data generated from Geant4-based calorimeter simulations, used for training and evaluating conditional diffusion models in gradient-based detector optimization. Each event represents the energy deposition profile of an electromagnetic shower under varying calorimeter configurations. The raw ROOT files were converted into normalized 2D histograms using Python with the uproot, numpy, matplotlib, and h5py libraries. For each event, two histograms represent the energy-deposition maps in orthogonal planes: images_xz: Energy deposition in the x–z plane. images_yz: Energy deposition in the y–z plane. Each histogram contains 100×100 bins, normalized per event. Labels and conditioning variables: labels_energy: Incident photon energies mapped as {1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 GeV}. labels_xy: Transverse cell size (granularity) of the calorimeter: {1, 2, 3, 4, 5 cm}. labels_z: Longitudinal cell depth: {4, 5, 8, 10, 15 cm}. labels_material: Absorber material type (PbF₂). File organization: total_photon_shower: Dataset used for diffusion model pre-training (100k events). post_total_photon_shower: Reduced dataset for LoRA fine-tuning (10k events). Generation details: Binning: 100×100 per 2D histogram. Automatic range scaling depending on detector geometry. Purpose:Designed for developing and benchmarking conditional diffusion models for calorimeter shower generation, surrogate modeling, and differentiable optimization of detector design parameters (e.g. geometry, segmentation, material). Format:HDF5 (.h5), fully compatible with Python’s h5py, numpy, and pandas.