AI energy-recon for SiPM-on-tile ZDC

Adds point-cloud AI-based energy reconstruction for the SiPM-on-tile version of the ZDC. This also changes the energy of the neutrons used in this detector's benchmark from 10 GeV to 20 GeV, since 10 GeV is outside of the range of validity for the model used for this reconstruction.

benchmarks/far_forward/analysis/make_plots_zdc_sipmontile_ai.py

+
+import numpy as np, pandas as pd, matplotlib.pyplot as plt
+import sys
+import compress_pickle as pickle
+
+infile=sys.argv[1]
+outdir=sys.argv[2]
+
+npz_unpacked = np.load(infile)
+
+predictions = npz_unpacked['outputs_scaled']  ### predicted value of the energy
+targets = npz_unpacked['targets_scaled']      ### true value of the energy
+
+print(dict(npz_unpacked))
+
+path_to_result_indv="benchmarks/far_forward/pointcloud/model/"
+
+E_truth     = targets[:,0]
+E_reco = predictions[::2]
+
+print(targets)
+print("E_truth", E_truth)
+print("E_reco", E_reco)
+
+#not used, since HEXPLIT provides a better theta resolution
+### True THETA
+#targets_plt_theta      = targets[:,1]*stdvs['theta'] + means['theta']
+### Predicted THETA
+#predictions_plt_theta  = predictions[:,1]*stdvs['theta'] + means['theta']
+
+plt.hist((E_reco-E_truth)/E_truth*100, bins=np.linspace(-30,30,30))
+
+plt.xlabel("$(E_{\\rm reco}-E_{truth})/E_{\\rm truth}$ [%]")
+plt.ylabel("events")
+plt.savefig(outdir+"/energy_residuals.png")