AGC Versions ================================ The below table gives a brief overview of all AGC versions. .. list-table:: AGC Versions :widths: 15 16 18 18 15 18 :header-rows: 1 * - Version - Datasets - Available Pipelines - Cuts - Machine Learning - Systematics * - 0.1.0 - CMS 2015 Open Data (POET) - Pure ``coffea``; ``coffea`` with ``ServiceX`` processors; ``ServiceX`` followed by ``coffea`` - Exactly one lepton with :math:`p_T>25` GeV; at least four jets with :math:`p_T>25` GeV; at least one jet with :math:`b`-tag > 0.5 - None - * - 0.2.0 - CMS 2015 Open Data (POET) - Pure ``coffea``; ``ServiceX`` followed by ``coffea`` - Exactly one lepton with :math:`p_T>25` GeV; at least four jets with :math:`p_T>25` GeV; at least one jet with :math:`b`-tag > 0.5 - None - * - 1.0.0 - CMS 2015 Open Data (NanoAOD) - Pure ``coffea``; ``ServiceX`` followed by ``coffea`` - Exactly one lepton with :math:`p_T>25` GeV; at least four jets with :math:`p_T>25` GeV; at least one jet with :math:`b`-tag > 0.5 - None - * - 2.0.0 - CMS 2015 Open Data (NanoAOD) - - - BDT to predict jet-parton assignment in :math:`t\bar{t}` events - Datasets ================================ The datasets used for the CMS :math:`t\bar{t}` notebook are from the 2015 CMS Open Data release. Versions 0.1.0 and 0.2.0 use ntuples generated using the `Physics Objects Extractor Tool (POET) `_. All versions >=1.0.0 use NanoAOD instead. The NanoAOD was generated from the 2015 CMS Open Data release using this pull request of CMSSW: `https://github.com/cms-sw/cmssw/pull/39040 `_. To set this up, the following commands should be run:: source /cvmfs/cms.cern.ch/cmsset_default.sh scram list CMSSW_10_6_ scram project CMSSW_10_6_30 cd CMSSW_10_6_30/ cmsenv cd src/ git cms-merge-topic 39040 ls -al scram build -j5 From this point, for data, you can use:: cmsDriver.py --python_filename doublemuon_cfg.py --eventcontent NANOAOD --customise Configuration/DataProcessing/Utils.addMonitoring --datatier NANOAOD --fileout file:doublemuon_nanoaod.root --conditions 106X_dataRun2_v36 --step NANO --filein file:doublemuon_miniaod.root --era Run2_25ns,run2_nanoAOD_106X2015 --no_exec --data -n -1 For MC, you can use:: cmsDriver.py --python_filename nanoaod15_cfg.py --eventcontent NANOAODSIM --customise Configuration/DataProcessing/Utils.addMonitoring --datatier NANOAODSIM --fileout file:nanoaod15.root --conditions 102X_mcRun2_asymptotic_v8 --step NANO --filein file:miniaod2015.root --era Run2_25ns,run2_nanoAOD_106X2015 --no_exec --mc -n -1 The code used to generate and subsequently merge these files is located in the following repository: `https://github.com/ekauffma/produce-nanoAODs `_ The data used is the same, regardless of MiniAOD vs NanoAOD. The list of datasets separated by process is included below: * **ttbar**: * nominal: * `19980 `_: Powheg + Pythia 8 (ext3), 2413 files, 3.4 TB -> converted * `19981 `_: Powheg + Pythia 8 (ext4), 4653 files, 6.4 TB -> converted * scale variation: * `19982 `_: same as below, unclear if overlap * `19983 `_: Powheg + Pythia 8 "scaledown" (ext3), 902 files, 1.4 TB -> converted * `19984 `_: same as below, unclear if overlap * `19985 `_: Powheg + Pythia 8 "scaleup" (ext3), 917 files, 1.3 TB -> converted * ME variation: * `19977 `_: same as below, unclear if overlap * `19978 `_: aMC@NLO + Pythia 8 (ext1), 438 files, 647 GB -> converted * PS variation: * `19999 `_: Powheg + Herwig++, 443 files, 810 GB -> converted * **single top**: * s-channel: * `19394 `_: aMC@NLO + Pythia 8, 114 files, 76 GB -> converted * t-channel: * `19406 `_: Powheg + Pythia 8 (antitop), 935 files, 1.1 TB -> converted * `19408 `_: Powheg + Pythia 8 (top), 1571 files, 1.8 TB -> converted * tW: * nominal: * `19412 `_: Powheg + Pythia 8 (antitop), 27 files, 30 GB -> converted * `19419 `_: Powheg + Pythia 8 (top), 23 files, 30 GB -> converted * DS: * `19410 `_: Powheg + Pythia 8 DS (antitop), 13 files, 15 GB * `19417 `_: Powheg + Pythia 8 DS (top), 13 files, 14 GB * scale variations: * `19415 `_: Powheg + Pythia 8 "scaledown" (antitop), 11 files, 15 GB * `19422 `_: Powheg + Pythia 8 "scaledown" (top), 13 files, 15 GB * `19416 `_: Powheg + Pythia 8 "scaleup" (antitop), 12 files, 14 GB * `19423 `_: Powheg + Pythia 8 "scaleup" (top), 13 files, 14 GB * there are also larger `NoFullyHadronicDecays` samples: `19411 `_, `19418 `_ * tZ / tWZ: potentially missing in inputs, not included in `/ST_*` * **W+jets**: * nominal (with 1l filter): * `20546 `_: same as below, unclear if overlap * `20547 `_: aMC@NLO + Pythia 8 (ext2), 5601 files, 4.5 TB -> converted * `20548 `_: aMC@NLO + Pythia 8 (ext4), 4598 files, 3.8 TB -> converted * **data**: * single muon: * `24119 `_: 1916 files, 1.4 TB -> converted * single electron: * `24120 `_: 2974 files, 2.6 TB -> converted * validated runs: * `24210 `_: single txt file More information about datasets can be found in `analysis-grand-challenge/datasets/cms-open-data-2015/ `_. Cuts ================================ For versions 0.1.0, 0.2.0, and 1.0.0, the cuts used are the following: * Leptons (electrons and muons) must have :math:`p_T>25` GeV * Events must contain exactly one lepton * Jets must have :math:`p_T>25` GeV * Events must have at least four jets * Jets are considered :math:`b`-tagged if they have a :math:`b`-tag score over `B_TAG_THRESHOLD=0.5`. * Events must have at least one :math:`b`-tagged jet * **4j1b Region**: Events must have exactly one :math:`b`-tagged jet * **4j2b Region**: Events must have two or more :math:`b`-tagged jets This is modified to better reflect common practices in CMS in subsequent versions, using the following cuts: * Leptons (electrons and muons) must have :math:`p_T>30` GeV, :math:`|\eta|<2.1`, and ``sip3d<4`` (significance of 3d impact parameter) * For electrons, we also require ``cutBased==4`` (tight) * For muons, we also require ``tightId`` and ``pfRelIso04_all<0.15`` (PF relative isolation dR=0.4, total (deltaBeta corrections)) * Events must contain exactly one lepton * Jets must have :math:`p_T>30` GeV, :math:`|\eta|>2.4`, and ``isTightLeptonVeto`` * Events must have at least four jets * Jets are considered :math:`b`-tagged if they have a :math:`b`-tag score over `B_TAG_THRESHOLD=0.5`. * Events must have at least one :math:`b`-tagged jet * **4j1b Region**: Events must have exactly one :math:`b`-tagged jet * **4j2b Region**: Events must have two or more :math:`b`-tagged jets