Omkar2118/datasets huggingface dataset:
MLPF
Full name: Simulated particle-level dataset of ttbar with PU200 using Pythia8+Delphes3 for machine learned particle flow (MLPF)

Dataset of 50,000 top quark-antiquark (ttbar) events produced in proton-proton collisions at 14 TeV, overlaid with minimum bias events corresponding to a pileup of 200 on average. The dataset consists of detector hits as the input, generator particles as the ground truth and reconstructed particles from DELPHES for additional validation.
The DELPHES model corresponds to a CMS-like detector with a multi-layered charged particle tracker, an electromagnetic and hadron calorimeter. Pythia8 and Delphes3 were used for the simulation.

Each file contains a bzip2-compressed python pickle with the following contents:
```
> data = pickle.load(bz2.BZ2File("out/pythia8_ttbar/tev14_pythia8_ttbar_0_0.pkl.bz2", "rb"))

# Each file contains lists of arrays X (detector elements), ygen (generator particles) and ycand (rule-based PF particles from Delphes) for 100 events
> len(data["ycand"]), len(data["ygen"]), len(data["X"])
100, 100, 100

#Each element in the list corresponds to an event. The first event in the file contains 5992 detector elements, ygen and ycand are 0-padded to the same length as X
> data["X"][0].shape, data["ygen"][0].shape, data["ycand"][0].shape, 
((5992, 12), (5992, 7), (5992, 7))

# The X rows are detector elements: calorimeter towers and tracks with the following 12-features (0-padded)
# tower: [type==1, Et (GeV), eta, sin phi, cos phi, E (GeV), Eem (GeV), Ehad (GeV), 0, 0, 0, 0]
# track: [type==2, pt (GeV), eta, sin phi, cos phi, P (GeV), eta_outer, sin phi_outer, cos phi_outer, charge, is_gen_muon, is_gen_electron]

# The ygen (ycand) rows are generator-level truth particles (rule-based PF particles from Delphes) with the following features:
# [pid, charge, pt (GeV), eta, sin phi, cos phi, E (GeV)]
# pid==0: placeholder/padding entry
# pid==1: charged hadrons
# pid==2: neutral hadrons
# pid==3: photons
# pid==4: electrons
# pid==5: muons
```

Introducing paper: MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks