Demonstration code for two-particle correlation neural network (2PCNN). See the reference for more information: https://arxiv.org/abs/1911.02020
- Python 3.7.1
- Keras 2.2.5
- TensorFlow 1.13.1
- prototype_train.py
a code to construct a 2PCNN prototype model with only energy flow information (pt, eta, phi for 2 particles), loading the jet data, and training with early stopping enabled. Recommened to increase the data statistics for a serious training.
- prototype_deploy.py
similar to the previous code, instead of training, only construct the same model, loading the trained weights, and produce the resulting scores and ROC curve.
- wgts_2pcnn_fatjet_w_vs_q.h5
- wgts_2pcnn_fatjet_t_vs_q.h5
pre-trained weights for W-jet versus quark-jet (2-prong versus 1-prong), and for top-jet versus quark-jet (3-prong versus 1-prong).
https://drive.google.com/drive/u/0/folders/1HojGLS_ODr7E7tndy2vz0fxRB2N10VAR
- fatjet_t_match_vz_to_tt.txt.gz
- fatjet_w_match_vz_to_ww.txt.gz
- fatjet_q_match_vz_to_qq.txt.gz
Samples for Top/W/quark jets, with anti-Kt algorithm of R=0.8. The samples were generated from a 2 TeV Z'->tt/WW/qq process and processed with Delphes for detector modeling. These are simple gziped text files (no needs of unzip). See the parse_jet_data() function in the demo code for how to read the data.