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Part 1: Document Description
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Citation |
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Title: |
pandas DataFrames of the DYToMuMu_M-20_CT10_TuneZ2star_v2_8TeV process |
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Identification Number: |
doi:10.60507/FK2/1MTTRE |
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Distributor: |
bonndata |
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Date of Distribution: |
2025-01-21 |
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Version: |
1 |
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Bibliographic Citation: |
Saala, Timo, 2025, "pandas DataFrames of the DYToMuMu_M-20_CT10_TuneZ2star_v2_8TeV process", https://doi.org/10.60507/FK2/1MTTRE, bonndata, V1 |
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Citation |
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Title: |
pandas DataFrames of the DYToMuMu_M-20_CT10_TuneZ2star_v2_8TeV process |
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Identification Number: |
doi:10.60507/FK2/1MTTRE |
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Authoring Entity: |
Saala, Timo (University of Bonn, Faculty of Mathematics and Natural Sciences, Physikalisches Institut) |
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Other identifications and acknowledgements: |
Saala Timo |
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Other identifications and acknowledgements: |
Schott Matthias |
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Producer: |
Schott Matthias |
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Software used in Production: |
pandas |
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Distributor: |
bonndata |
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Access Authority: |
Saala, Timo |
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Depositor: |
Saala, Timo |
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Date of Deposit: |
2024-12-23 |
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Holdings Information: |
https://doi.org/10.60507/FK2/1MTTRE |
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Study Scope |
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Keywords: |
Computer and Information Science, Physics |
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Abstract: |
This dataset contains pandas DataFrames that represent filtered versions of CMS Open Data (in the form of ROOT files) available on the CERN OpenData Portal. This dataset specifically contains data from a DYToMuMu process (Drell-Yan process resulting in two Muons in the final state), which is a simulated process created during the 2012 LHC run. <br> <br> A total of 121 (99 for real collision data) relevant variables are contained in the filtered pandas DataFrames that can be found here. A list of variables can be found below, for a full explanation of them, please refer to the following paper (PLACEHOLDER, REFERENCE PAPER HERE): <br> nEvent, runNum, lumisection, evtNum; <br> nMuon, vecMuon_PT, vecMuon_Eta, vecMuon_Phi, vecMuon_PTErr, vecMuon_Q, vecMuon_StaPt, vecMuon_StaEta, vecMuon_StaPhi, vecMuon_TrkIso03, vecMuon_EcalIso03, vecMuon_HcalIso03; <br> nVertex, vecVertex_nTracksfit, vecVertex_ndof, vecVertex_Chi2, vecVertex_X, vecVertex_Y, vecVertex_Z; <br> nEle, vecEle_PT, vecEle_Eta, vecEle_Phi, vecEle_Q, vecEle_TrkIso03, vecEle_EcalIso03, vecEle_HcalIso03, vecEle_D0, vecEle_Dz; <br> nTau, vecTau_PT, vecTau_Eta, vecTau_Phi, vecTau_Q, vecTau_RawIso3Hits, vecTau_RawIsoMVA3oldDMwoLT, vecTau_RawIsoMVA3oldDMwLT, vecTau_RawIsoMVA3newDMwoLT, vecTau_RawIsoMVA3newDMwLT; <br> nPhoton, vecPhoton_PT, vecPhoton_Eta, vecPhoton_Phi, vecPhoton_Hovere, vecPhoton_Sthovere, vecPhoton_HasPixelSeed, vecPhoton_IsConv, vecPhoton_PassElectronVeto; <br> nMctruth, vecMctruth_PT, vecMctruth_Eta, vecMctruth_Phi, vecMctruth_Id_1, vecMctruth_Id_2, vecMctruth_X_1, vecMctruth_X_2, vecMctruth_PdgId, vecMctruth_Status, vecMctruth_Y, vecMctruth_Mass, vecMctruth_Mothers.first, vecMctruth_Mothers.second; <br> nJets, vecJet_PT, vecJet_Eta, vecJet_Phi, vecJet_D0, vecJet_Dz, vecJet_nCharged, vecJet_nNeutrals, vecJet_nParticles, vecJet_Beta, vecJet_BetaStar, vecJet_dR2Mean, vecJet_Q, vecJet_Mass, vecJet_Area, vecJet_Energy, vecJet_chEmEnergy, vecJet_neuEmEnergy, vecJet_chHadEnergy, vecJet_neuHadEnergy, vecJet_ID, vecJet_Num, vecJet_mcFlavor, vecJet_GenPT, vecJet_GenEta, vecJet_GenPhi, vecJet_GenMass, vecJet_flavorMatchPT, vecJet_JEC, vecJet_MatchIdx; <br> nPF, vecPF_PT, vecPF_Eta, vecPF_Phi, vecPF_Mass, vecPF_E, vecPF_Q, vecPF_PfType, vecPF_EcalE, vecPF_HcalE, vecPF_ndof, vecPF_Chi2, vecPF_pvId, vecPF_X, vecPF_Y, vecPF_Z, vecPF_JetNum; <br> fMET_PT, fMET_Eta, fMET_Phi; <br> HLT_Mu17_Mu8, HLT_Mu24, HLT_MET120_v, HLT_Ele27, HLT_HT350. <br> <br> For the datasets containing data from real collisions at the LHC, the following variables are NOT contained: <br> nMctruth, vecMctruth_PT, vecMctruth_Eta, vecMctruth_Phi, vecMctruth_Id_1, vecMctruth_Id_2, vecMctruth_X_1, vecMctruth_X_2, vecMctruth_PdgId, vecMctruth_Status, vecMctruth_Y, vecMctruth_Mass, vecMctruth_Mothers.first, vecMctruth_Mothers.second; <br> vecJet_mcFlavor, vecJet_GenPT, vecJet_GenEta, vecJet_GenPhi, vecJet_GenMass, vecJet_flavorMatchPT, vecJet_JEC, vecJet_MatchIdx <br> |
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Kind of Data: |
Tabular Data (pandas DataFrames) |
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Notes: |
This dataset is derived from datasets found on the CERN OpenData portal. The original dataset is a simulated dataset from the 2012 run at the LHC, representing a Drell-Yan process resulting in two final state Muons. <br> The accompanying thumbnail picture was taken from the CMS Collaboration: https://cds.cern.ch/record/2841512 |
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Methodology and Processing |
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Sources Statement |
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Data Sources: |
CMS collaboration (2017). Simulated dataset DYToMuMu_M-20_CT10_8TeV-powheg-pythia6 in AODSIM format for 2012 collision data. CERN Open Data Portal. https://doi.org/10.7483/OPENDATA.CMS.UQL1.0C31 |
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Characteristics of Source Notes: |
CMS Open Data from the CERN OpenData Portal |
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Documentation and Access to Sources: |
Website: https://opendata.cern.ch/ Documentation: https://opendata.cern.ch/search?q=&f=type%3ADocumentation&l=list&order=desc&p=1&s=10&sort=mostrecent Everything is openly accessible |
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Data Access |
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Other Study Description Materials |
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Related Materials |
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A GitHub repository containing all necessary code and a tutorial in order to re-produce the given dataset, or produce more data from either the same or different original CMS Open Data can be found <a href="https://github.com/TSaala/OpenDataToDataFrame"> here </a> |
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Related Publications |
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Citation |
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Title: |
CMS collaboration (2017). Simulated dataset DYToMuMu_M-20_CT10_8TeV-powheg-pythia6 in AODSIM format for 2012 collision data. CERN Open Data Portal. DOI:10.7483/OPENDATA.CMS.UQL1.0C31 |
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Identification Number: |
10.7483/OPENDATA.CMS.UQL1.0C31 |
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Bibliographic Citation: |
CMS collaboration (2017). Simulated dataset DYToMuMu_M-20_CT10_8TeV-powheg-pythia6 in AODSIM format for 2012 collision data. CERN Open Data Portal. DOI:10.7483/OPENDATA.CMS.UQL1.0C31 |
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Citation |
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Title: |
Timo Saala and Matthias Schott. Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning. https://arx2501.06896iv.org/abs/ . 2025 |
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Identification Number: |
2501.06896 |
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Bibliographic Citation: |
Timo Saala and Matthias Schott. Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning. https://arx2501.06896iv.org/abs/ . 2025 |
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DYToMuMu_M-20_CT10_TuneZ2star_v2_8TeV_0.feather |
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application/octet-stream |
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DYToMuMu_M-20_CT10_TuneZ2star_v2_8TeV_1.feather |
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application/octet-stream |
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DYToMuMu_M-20_CT10_TuneZ2star_v2_8TeV_2.feather |
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application/octet-stream |
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application/octet-stream |
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application/octet-stream |
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application/octet-stream |
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application/octet-stream |
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DYToMuMu_M-20_CT10_TuneZ2star_v2_8TeV_7.feather |
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application/octet-stream |
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DYToMuMu_M-20_CT10_TuneZ2star_v2_8TeV_8.feather |
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application/octet-stream |
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DYToMuMu_M-20_CT10_TuneZ2star_v2_8TeV_9.feather |
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application/octet-stream |
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Example.py |
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text/x-python |
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README.txt |
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text/plain |