Near to Far Translation

A generative model based on min-gpt. It learns the conditional distribution over the fd recontructed variables given nd reconstructed variables: $$p(x_{FD} | x_{ND})$$

We learn this autoregressively, i.e the transformer is trained to predict: $$p(x_{FD} | x_{ND}) = \prod_i p(x_{i_{FD}}| x_{1_{FD}}, x_{2_{FD}}, ..., x_{i-1_{FD}}, x_{ND}) $$

We learn each fd dimension as Gaussian Mixture distribution that has been changed in some way. For the CVN scores we transform the distribution using a sigmoid to ensure it stays within the range $[0, 1]$ and for the fd energies we transform them using the exponential to ensure that the distribution is strictly positive and a with a tail.

Usage

• Generate Data (see Paired Dataset section)

• Change the data_path in NewPairedData in the gpt.dataset.py script (see how to download the dataset below)

• Train using python3 gpt_train.py <data_path> <work_dir>.

• Then use the gpt_sample.ipynb notebook to generate new events and make plots.

Vertices

Vertices from the training data in data/train_vertices.npy in shape n_samples, 3 The columns are in order x, y, z.

Software Requirements

wtih pip (also possible with conda):

pip install -r requirements.txt

TO-dos and Issues

• - Explore other generative models. Any conditional generative models should work (diffusion, normalising flows, GANs)

• - Make the input and output reco vars configurables from the CLI and stored in the config file rather than just hardcoding it into gpt/dataset.py with a bunch of them commented out

• - Try normalising variables to [0, 1]

Paired Dataset

A paired dataset .h5 file is required for training. This should be the ndfd reconstruction from the paired dataset simulation that has been concatenated into a single file. An example can be downloaded from CERNBOX. Then use the provided script to apply cuts to the data.

python3 scripts/cut.py --in_fname <ndfd_reco_h5_name> --out_fname <output_csv_name> /path/to/datadir/

This will create a <output_csv_name> csv file in /path/to/datadir/ from the <ndfd_reco_h5_name> h5 file also in /path/to/datadir/. The csv file is used as the input file for training.

Variables

Far Detector	CVN scores	Near Detector	ND Reco	Global
fd_numu_nu_E	fd_numu_score	nuPDG	Ev_reco	run
fd_numu_had_E	fd_nue_score	nuMomX	Elep_reoo	eventID
fd_numu_lep_E	fd_nc_score	NuMomY	theta_reco	isCC
fd_numu_recomethod	fd_nutau_score	NuMomZ	eRecoP	nuPDG
numu_longesttrackcontained	fd_antinu_score	Ev	eRecoN	vtxX
fd_numu_trackmommethod	fd_proton0_score	mode	eRecoPip	vtxY
fd_nue_nu_E	fd_proton1_score	LepPDG	eRecoPim	vtxZ
fd_nue_had_E	fd_proton2_score	LepMomX	eRecoPi0
fd_nue_lep_E	fd_protonN_score	LepMomY	eRecoOther
fd_nue_recomethod	fd_pion0_score	LepMomZ
fd_nc_nu_E	fd_pion1_score	lepE
fd_nc_had_E	fd_pion2_score	LepNuAngle
fd_nc_lep_E	fd_pionN_score	nP
fd_nc_recomethod	fd_pionzero0_score	nN
	fd_pionzero1_score	nPip
	fd_pionzero2_score	nPim
	fd_pionzeroN_score	nPi0
	fd_neutron0_score	nKp
	fd_neutron1_score	nKm
	fd_neutron2_score	nK0
	fd_neutronN_score	ne
		nOther
		nNucleus
		nUNKOWN
		eP
		eN
		ePip
		ePim
		ePi0
		eOther
		reco_numu
		reco_nue
		reco_nc
		reco_q
		muon_contained
		muon_tracker
		muon_ecal
		muon_exit
		reco_lepton_pdg
		muon_endpntX
		muon_endpntY
		muon_endpntZ
		Ehad_veto
		muon_endVolName