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Merge branch 'main' of https://github.com/rkansal47/HHbbVV
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rkansal47 committed Mar 15, 2024
2 parents 2a9fe3f + 1278eab commit 3c86223
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228 changes: 228 additions & 0 deletions src/HHbbVV/VBF_binder/VBFKinematicsStudy.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "7b82bb87",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import mplhep as hep\n",
"import matplotlib.ticker as mticker\n",
"import numpy as np\n",
"\n",
"import uproot\n",
"import awkward as ak\n",
"from coffea import nanoevents\n",
"\n",
"from coffea.nanoevents.methods.base import NanoEventsArray\n",
"from coffea.analysis_tools import Weights, PackedSelection\n",
"from coffea.nanoevents.methods import nanoaod\n",
"from coffea.nanoevents.methods import vector\n",
"from coffea.lookup_tools.dense_lookup import dense_lookup\n",
"\n",
"from HHbbVV.processors.utils import pad_val\n",
"\n",
"plt.style.use(hep.style.CMS)\n",
"hep.style.use(\"CMS\")\n",
"formatter = mticker.ScalarFormatter(useMathText=True)\n",
"formatter.set_powerlimits((-3, 3))\n",
"plt.rcParams.update({\"font.size\": 24})"
]
},
{
"cell_type": "markdown",
"id": "09429b8f",
"metadata": {},
"source": [
"Look at single SM VBF HH signal NanoAOD file"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42e7add7",
"metadata": {},
"outputs": [],
"source": [
"events = nanoevents.NanoEventsFactory.from_root(\n",
" \"root://cmseos.fnal.gov///store/user/lpcpfnano/cmantill/v2_3/2018/HH/VBF_HHTobbVV_CV_1_C2V_1_C3_1_TuneCP5_13TeV-madgraph-pythia8/VBF_HHTobbVV_CV_1_C2V_1_C3_1/220808_150149/0000/nano_mc2018_1-1.root\",\n",
" schemaclass=nanoevents.NanoAODSchema,\n",
").events()\n",
"\n",
"Z_PDGID = 23\n",
"W_PDGID = 24\n",
"HIGGS_PDGID = 25\n",
"GEN_FLAGS = [\"fromHardProcess\", \"isLastCopy\"]"
]
},
{
"cell_type": "markdown",
"id": "7f076483",
"metadata": {},
"source": [
"Get generator-level Higgs and Vs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1dfff6ce",
"metadata": {},
"outputs": [],
"source": [
"higgs = events.GenPart[\n",
" (abs(events.GenPart.pdgId) == HIGGS_PDGID) * events.GenPart.hasFlags(GEN_FLAGS)\n",
"]\n",
"\n",
"vs = events.GenPart[((abs(events.GenPart.pdgId) == 24)) * events.GenPart.hasFlags(GEN_FLAGS)]"
]
},
{
"cell_type": "markdown",
"id": "398dfc82",
"metadata": {},
"source": [
"Reproduce AK4 jet selections from skimmer"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "748e3109",
"metadata": {},
"outputs": [],
"source": [
"ak4_jet_selection = { # noqa: RUF012\n",
" \"pt\": 25,\n",
" \"eta\": 2.7,\n",
" \"jetId\": \"tight\",\n",
" \"puId\": \"medium\",\n",
" \"dR_fatjetbb\": 1.2,\n",
" \"dR_fatjetVV\": 0.8,\n",
"}\n",
"\n",
"# ak8 jet preselection\n",
"preselection = { # noqa: RUF012\n",
" \"pt\": 300.0,\n",
" \"eta\": 2.4,\n",
" \"VVmsd\": 50,\n",
" # \"VVparticleNet_mass\": [50, 250],\n",
" # \"bbparticleNet_mass\": [92.5, 162.5],\n",
" \"bbparticleNet_mass\": 50,\n",
" \"VVparticleNet_mass\": 50,\n",
" \"bbFatJetParticleNetMD_Txbb\": 0.8,\n",
" \"jetId\": 2, # tight ID bit\n",
" \"DijetMass\": 800, # TODO\n",
" # \"nGoodElectrons\": 0,\n",
"}\n",
"\n",
"num_jets = 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b03b61b",
"metadata": {},
"outputs": [],
"source": [
"fatjets = events.FatJet\n",
"\n",
"# particlenet xbb vs qcd\n",
"\n",
"txbb = pad_val(\n",
" fatjets.particleNetMD_Xbb / (fatjets.particleNetMD_QCD + fatjets.particleNetMD_Xbb),\n",
" num_jets,\n",
" axis=1,\n",
")\n",
"\n",
"# bb VV assignment\n",
"\n",
"bb_mask = txbb[:, 0] >= txbb[:, 1]\n",
"bb_mask = np.stack((bb_mask, ~bb_mask)).T"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "629523e9",
"metadata": {},
"outputs": [],
"source": [
"jets = events.Jet\n",
"\n",
"# dR_fatjetVV = 0.8 used from last two cells of VBFgenInfoTests.ipynb with data generated from SM signal vbf\n",
"# https://github.com/rkansal47/HHbbVV/blob/vbf_systematics/src/HHbbVV/VBF_binder/VBFgenInfoTests.ipynb\n",
"# (0-14R1R2study.parquet) has columns of different nGoodVBFJets corresponding to R1 and R2 cuts\n",
"vbf_jet_mask = (\n",
" jets.isTight\n",
" & (jets.pt > ak4_jet_selection[\"pt\"])\n",
" & (np.abs(jets.eta) < 4.7)\n",
" # medium puId https://twiki.cern.ch/twiki/bin/viewauth/CMS/PileupJetIDUL\n",
" & ((jets.pt > 50) | ((jets.puId & 2) == 2))\n",
" & (\n",
" ak.all(\n",
" jets.metric_table(\n",
" ak.singletons(ak.pad_none(fatjets, num_jets, axis=1, clip=True)[bb_mask])\n",
" )\n",
" > ak4_jet_selection[\"dR_fatjetbb\"],\n",
" axis=-1,\n",
" )\n",
" )\n",
" & (\n",
" ak.all(\n",
" jets.metric_table(\n",
" ak.singletons(ak.pad_none(fatjets, num_jets, axis=1, clip=True)[~bb_mask])\n",
" )\n",
" > ak4_jet_selection[\"dR_fatjetVV\"],\n",
" axis=-1,\n",
" )\n",
" )\n",
")\n",
"\n",
"vbf_jets = jets[vbf_jet_mask]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2998910",
"metadata": {},
"outputs": [],
"source": [
"etas = pad_val(vbf_jets.eta, 2, axis=1)\n",
"\n",
"plt.rcParams.update({\"font.size\": 24})\n",
"plt.figure(figsize=(12, 12))\n",
"plt.hist(np.abs(etas[:, 0] - etas[:, 1]), np.arange(0, 6, 0.25), histtype=\"step\", density=True)\n",
"plt.xlabel(r\"$\\eta_{jj}$\")\n",
"plt.ylabel(\"Events (A. U.)\")\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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