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Merged
smuzaffar merged 1 commit into from
Oct 12, 2023
Merged

Add PartcileNet ONNX models for Run 3 PFScouting #49

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12 changes: 12 additions & 0 deletions Run3Scouting/ParticleNetAK4/V00/README.md
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# ParticleNetAK4-CHS-V00

This folder contains the ONNX models for the `ParticleNetAK4` tagger designed for AK4 jet tagging (i.e., b-/c-tagging, q/g-tagging, PU jet ID). The model is trained on Run 3 scouting AK4PF jets.

The output classes of the DNN are:
- probb
- probbb
- probc
- probcc
- probuds
- probg
- probundef
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256 changes: 256 additions & 0 deletions Run3Scouting/ParticleNetAK4/V00/preprocess.json
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{
"output_names": [
"label_b",
"label_bb",
"label_c",
"label_cc",
"label_uds",
"label_g",
"label_undef"
],
"input_names": [
"pf_points",
"pf_features",
"pf_mask"
],
"pf_points": {
"var_names": [
"pfcand_etarel",
"pfcand_phirel"
],
"var_infos": {
"pfcand_etarel": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_phirel": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
}
},
"min_length": 16,
"max_length": 100
},
"pf_features": {
"var_names": [
"pfcand_pt_log_nopuppi",
"pfcand_e_log_nopuppi",
"pfcand_etarel",
"pfcand_phirel",
"pfcand_abseta",
"pfcand_charge",
"pfcand_isEl",
"pfcand_isMu",
"pfcand_isGamma",
"pfcand_isChargedHad",
"pfcand_isNeutralHad",
"pfcand_lostInnerHits",
"pfcand_normchi2",
"pfcand_quality",
"pfcand_dz",
"pfcand_dzsig",
"pfcand_dxy",
"pfcand_dxysig",
"pfcand_btagEtaRel",
"pfcand_btagPtRatio",
"pfcand_btagPParRatio"
],
"var_infos": {
"pfcand_pt_log_nopuppi": {
"median": 0.8,
"norm_factor": 0.7,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_e_log_nopuppi": {
"median": 1.2,
"norm_factor": 0.6,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_etarel": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_phirel": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_abseta": {
"median": 0.8,
"norm_factor": 1.2,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_charge": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_isEl": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_isMu": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_isGamma": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_isChargedHad": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_isNeutralHad": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_lostInnerHits": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
},
"pfcand_normchi2": {
"median": 5,
"norm_factor": 0.2,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_quality": {
"median": 0,
"norm_factor": 0.2,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_dz": {
"median": 0,
"norm_factor": 150,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_dzsig": {
"median": 0,
"norm_factor": 0.7,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_dxy": {
"median": 0,
"norm_factor": 270,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_dxysig": {
"median": 0,
"norm_factor": 1.1,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_btagEtaRel": {
"median": 2.1,
"norm_factor": 0.5,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_btagPtRatio": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
},
"pfcand_btagPParRatio": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -5,
"upper_bound": 5,
"pad": 0
}
},
"min_length": 16,
"max_length": 100
},
"pf_mask": {
"var_names": [
"pfcand_mask"
],
"var_infos": {
"pfcand_mask": {
"median": 0,
"norm_factor": 1,
"replace_inf_value": 0,
"lower_bound": -1e+32,
"upper_bound": 1e+32,
"pad": 0
}
},
"min_length": 16,
"max_length": 100
}
}
10 changes: 10 additions & 0 deletions Run3Scouting/ParticleNetAK8/General/V00/README.md
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# ParticleNetAK8-General-V00

This folder contains the ONNX models for the ParticleNet tagger designed for identifying the Higgs boson and its decay modes. No mass decorrelation is performed. The model is trained on Run 3 scouting AK8PF jets.

The output classes of the DNN are:
- probHbb
- probHcc
- probHqq
- probQCDall

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