mc16_13TeV:mc16_13TeV.410470.PhPy8EG_A14_ttbar_hdamp258p75_nonallhad.merge.AOD.e6337_e5984_s3126_r9364_r9315 mc16_13TeV:mc16_13TeV.410470.PhPy8EG_A14_ttbar_hdamp258p75_nonallhad.merge.AOD.e6337_e5984_s3126_r10201_r10210 mc16_13TeV:mc16_13TeV.410470.PhPy8EG_A14_ttbar_hdamp258p75_nonallhad.merge.AOD.e6337_e5984_s3126_r10724_r10726

ttbar sample MUON5 DxAOD dsid

mc16_13TeV.410470.PhPy8EG_A14_ttbar_hdamp258p75_nonallhad.deriv.DAOD_MUON5.e6337_e5984_s3126_r9364_r9315_p3980 22.411 TB, 119432000 mc16_13TeV.410470.PhPy8EG_A14_ttbar_hdamp258p75_nonallhad.deriv.DAOD_MUON5.e6337_e5984_s3126_r10201_r10210_p3980 mc16_13TeV.410470.PhPy8EG_A14_ttbar_hdamp258p75_nonallhad.deriv.DAOD_MUON5.e6337_e5984_s3126_r10201_r10210_p3980

Zmumugam sample xAOD dsid

mc16_13TeV:mc16_13TeV.366145.Sh_224_NN30NNLO_mumugamma_LO_pty_7_15.merge.AOD.e7006_e5984_s3126_r9364_r9315 Total events : 999000 mc16_13TeV:mc16_13TeV.366146.Sh_224_NN30NNLO_mumugamma_LO_pty_15_35.merge.AOD.e7006_e5984_s3126_r9364_r9315 Total events : 3995000 mc16_13TeV:mc16_13TeV.366147.Sh_224_NN30NNLO_mumugamma_LO_pty_35_70.merge.AOD.e7006_e5984_s3126_r9364_r9315 Total events : 499000 mc16_13TeV:mc16_13TeV.366148.Sh_224_NN30NNLO_mumugamma_LO_pty_70_140.merge.AOD.e7006_e5984_s3126_r9364_r9315 Total events : 250000 mc16_13TeV:mc16_13TeV.366149.Sh_224_NN30NNLO_mumugamma_LO_pty_140_E_CMS.merge.AOD.e7006_e5984_s3126_r9364_r9315 Total events : 250000 mc16_13TeV:mc16_13TeV.366145.Sh_224_NN30NNLO_mumugamma_LO_pty_7_15.merge.AOD.e7006_e5984_s3126_r10201_r10210 Total events : 1246000 mc16_13TeV:mc16_13TeV.366146.Sh_224_NN30NNLO_mumugamma_LO_pty_15_35.merge.AOD.e7006_e5984_s3126_r10201_r10210 Total events : 4985000 mc16_13TeV:mc16_13TeV.366147.Sh_224_NN30NNLO_mumugamma_LO_pty_35_70.merge.AOD.e7006_e5984_s3126_r10201_r10210 Total events : 624000 mc16_13TeV:mc16_13TeV.366148.Sh_224_NN30NNLO_mumugamma_LO_pty_70_140.merge.AOD.e7006_e5984_s3126_r10201_r10210 Total events : 319000 mc16_13TeV:mc16_13TeV.366149.Sh_224_NN30NNLO_mumugamma_LO_pty_140_E_CMS.merge.AOD.e7006_e5984_s3126_r10201_r10210 Total events : 320000 mc16_13TeV:mc16_13TeV.366145.Sh_224_NN30NNLO_mumugamma_LO_pty_7_15.merge.AOD.e7006_e5984_s3126_r10724_r10726 Total events : 1670000 mc16_13TeV:mc16_13TeV.366146.Sh_224_NN30NNLO_mumugamma_LO_pty_15_35.merge.AOD.e7006_e5984_s3126_r10724_r10726 Total events : 6461000 mc16_13TeV:mc16_13TeV.366147.Sh_224_NN30NNLO_mumugamma_LO_pty_35_70.merge.AOD.e7006_e5984_s3126_r10724_r10726 Total events : 834000 mc16_13TeV:mc16_13TeV.366148.Sh_224_NN30NNLO_mumugamma_LO_pty_70_140.merge.AOD.e7006_e5984_s3126_r10724_r10726 Total events : 418000 mc16_13TeV:mc16_13TeV.366149.Sh_224_NN30NNLO_mumugamma_LO_pty_140_E_CMS.merge.AOD.e7006_e5984_s3126_r10724_r10726 Total events : 250000

RNN INPUT samples

Zgam mini-ntup samples (Full run-2) atint:/net/ustc_03/prompt/MININTUP/zgam_mc_fullrun2.root
Zgam mini-ntup samples atint:/net/ustc_03/prompt/MININTUP/zgam_mca[d,e]
Zgam ntup samples atint:/net/ustc_03/prompt/NTUP/zgam_mca[d,e]

Data

Penn EOS storage space:

ssh -XY pennww@lxplus.cern.ch
/eos/atlas/atlascerngroupdisk/penn-ww/

Setup atlas environment in release 21

Follow instructions here: https://atlassoftwaredocs.web.cern.ch/gittutorial/
- Note that a user has to fork repository from gitlab.cern.ch: https://atlassoftwaredocs.web.cern.ch/gittutorial/gitlab-fork/
- Go to the 21.2 athena release
- After the fork instructions stop using this guide (but refer back to how the workflow works, like tags and such).
Setup acm with instructions found here: https://twiki.cern.ch/twiki/bin/viewauth/AtlasComputing/CMTCMakeRosettaStone
- Use this release when following the instructions:

acmSetup --sourcedir=../source AthDerivation,21.2,21.2.3.0

Checkout packages:

acm add_pkg athena/PhysicsAnalysis/DerivationFramework/DerivationFrameworkHiggs
acm add_pkg athena/PhysicsAnalysis/JetTagging/JetTagNonPromptLepton

Download AOD for testing (e.g. mc16_13TeV.410501.PowhegPythia8EvtGen_A14_ttbar_hdamp258p75_nonallhad.merge.AOD.e5458_s3126_r9364_r9315) and run derivation with command:

Reco_tf.py --inputAODFile input_AOD.pool.root --outputDAODFile output.pool.root --reductionConf HIGG8D1 --maxEvents 100

Internal checks of JetTagNonPromptLepton can be run on MUON5 derivations, with commands like this:

athena share/JetTagNonPromptLepton_decorate.py -c 'inputDir="{MUON5_file}";EvtMax=10'

When making merge requests be sure to add a "sweep:ignore" label, which stops git trying to merge the 21.2 development branch (the only place JetTagNonPromptLepton lives) with master. Also add "Derivation" label.

Summary of taggers

SV1
- Uses loosest track selection with pT>400 MeV
JetFitter is probably most complex tagger
- Uses tracks with pT > 786 MeV
- Difficult to change configuration and to understand and interpret results
- Requires substantial time investment to learn how to operate
IP3D is most optimal for low and medium pT jets (based on BDT weights of input variables)
- Uses tracks with pT > 1000 MeV
- Requires training files with PDFs
- Produce training files by setting doComputeReference=True
- Can we train only for our specific topology?
- Important information in sign of impact parameter
Variable size cone for track selection
- 0.45 for jet pT=20 GeV

Initial plan

Make full b-tagging ntuples with all tracks
- ReduceInfo =False
- Saves all reconstructed tracks
- Flag for which tagger used this track
- Flag for whether track matches a particle from b-hadron
Study which tracks are used by IPD3
- Fraction of b-hadron tracks used for different selection
Change track selection for IP3D
- Create new references
- Rerun with new references

Proposed tasks for b-tagging algorithm group

Compare MV2 with standalone IP3D
- Can IP3D alone perform as well as MV2 for this topology?
- Add default IP3D to our ntuples
- Also compare returned IP3D with MV2
Switch to using b-tagging performance ntuples with all tracks
- Check that muon variables are saved: pT, eta, phi, isolation and impact variables
- Add muon truth matching and parent truth information
Study track selection for IP3D
- Make plots as in page 3 of Valerio's talk on September 17
- Study track pT, DR, etc - find best track selection for loose isolated lepton topology
Train new IP3D with updated track selection
- Create new references and repeat our study
Longer term issues
- Does fake rejection depends on pileup? What is pileup and underlying event systematic?
- Study electrons

Ideas for improvements

Can we reconstruct K0 to pi+pi- decays? Does this help to identify B decays?
Does it help to select a reconstructed ID track which has a highest impact parameter when combined with muon track?
Make event displays to understand how b-tag veto can be improved
Systematic uncertainty: correlations between b-tagging weight and isolation variables

Tasks

Current task list

Develop and document standalone tool - if necesseary
- Code and instructions for adding necessary input variables to derivations: track jets and flavour tagging
- Code and instructions for standalone dual tool that runs on xAOD
Optimise saving of input and output variables in xAOD
- Different prefixes for input and output variables
- Add python confirguration for saving only BDT weights into DxAOD (to save space for CP derivations)
Merge package branch and trunk

Working with data

The calculation of the fake efficiency of whatever algorithm we use can be done by
- Looking at the inclusive muon spectrum, whilst subtracting the W and Z components to look at the multijet background.
- Looking at the 2lSS ttbar control region: 2 or 3 jets, 2 same sign leptons. The problem with this is that there is a sizeable ttbarW background to disentangle - which would be done with Monte Carlo.
The easier of the two is looking at the control region - this is the method that we will use for ICHEP.
The other method is less biased but harder - to keep in mind for the long term.

To-do list (updated 12-01-17)

Truth study tasks

Do full truth study of lepton origins, including non-prompt and charge misid - done.
Plot BDT for electrons with direct tau parent to see if the BDT biases against taus which have similar lifetime to B hadrons - done.

BDT optimisation tasks

Add pT(lepton)/pT(jet) and dR(lepton, jet) variables in BDT to see how rejection improves - done
Have an "all-in-one" BDT including isolation, impact parameter etc. and a "b-tagging only" BDT - done

Ideas for updates

Check pTRel variable - like pT(lepton)/pT(jet)
Remove one of ip2 or ip3
Use both 410000 and 410009 for training to improve statistics

Topic revision: r117 - 2022-02-14 - RustemOspanov

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