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Add ParticleNet to NanoAOD #31096
Add ParticleNet to NanoAOD #31096
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The code-checks are being triggered in jenkins. |
+code-checks Logs: https://cmssdt.cern.ch/SDT/code-checks/cms-sw-PR-31096/17641
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A new Pull Request was created by @hqucms (Huilin Qu) for master. It involves the following packages: PhysicsTools/NanoAOD @gouskos, @cmsbuild, @fgolf, @mariadalfonso, @santocch, @peruzzim can you please review it and eventually sign? Thanks. cms-bot commands are listed here |
@cmsbuild please test |
The tests are being triggered in jenkins.
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+1 |
Comparison job queued. |
This PR add 9 floats for a total of 37 floats of ID for the AK8 ? cmssw/PhysicsTools/NanoAOD/python/jets_cff.py Lines 408 to 439 in 872f4c7
cmssw/PhysicsTools/NanoAOD/python/jets_cff.py Lines 398 to 403 in 872f4c7
Are the SF available for all these ? |
@@ -313,18 +317,21 @@ def nanoAOD_customizeCommon(process): | |||
nanoAOD_addDeepBTag_switch = cms.untracked.bool(False), | |||
nanoAOD_addDeepBoostedJet_switch = cms.untracked.bool(True), # will deactivate this in future miniAOD releases | |||
nanoAOD_addDeepDoubleX_switch = cms.untracked.bool(True), | |||
nanoAOD_addParticleNet_switch = cms.untracked.bool(True), |
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do you need to re-run the particleNet also when is available in the mini ?
For the re-mini we can use the one stored in the miniAOD ?
For the old samples [ run2_nanoAOD_94X2016, run2_nanoAOD_94XMiniAODv1, run2_nanoAOD_94XMiniAODv2, run2_nanoAOD_102Xv1, run2_nanoAOD_106Xv1 ] indeed we need to re-run.
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do you need to re-run the particleNet also when is available in the mini ?
No. We can use the ones stored in MiniAOD once they are available (e.g., in UL re-mini).
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ok. good the taggers are already available in master version.
--> for master:
nanoAOD_produceParticleNet_switch = cms.untracked.bool(False)
if run2_nanoAOD_106Xv1 + anyOld:
nanoAOD_produceParticleNet_switch = cms.untracked.bool(True)
add deepParticleNet
for 10_6:
if NOT run2_miniAOD_devel:
nanoAOD_produceParticleNet_switch = cms.untracked.bool(True)
add ParticleNet
probably same logic should be applied to all the previous tagger
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ok. good the taggers are already available in master version.
--> for master:
nanoAOD_produceParticleNet_switch = cms.untracked.bool(False)
if run2_nanoAOD_106Xv1 + anyOld:
nanoAOD_produceParticleNet_switch = cms.untracked.bool(True)
add deepParticleNetfor 10_6:
if NOT run2_miniAOD_devel:
nanoAOD_produceParticleNet_switch = cms.untracked.bool(True)
add ParticleNetprobably same logic should be applied to all the previous tagger
Yes indeed. Shall I change this or would you prefer to do it centrally afterwards?
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should be done here and not afterwards
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should be done here and not afterwards
Done in 2c7b9ae.
@mariadalfonso Also given that on average the number of high-pt AK8 jets in typical events (e.g., ttbar) is <<1, the overall space increase should be quite small. |
Comparison is ready Comparison Summary:
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The tests are being triggered in jenkins.
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+1 |
Comparison job queued. |
Comparison is ready @slava77 comparisons for the following workflows were not done due to missing matrix map:
Comparison Summary:
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+1 |
@qliphy |
Maybe this is for @hqucms ? |
Increase in Mini is negligible (1 out of 110 b-tag discriminants per ak8 jet). [1] before: http://hqu.web.cern.ch/hqu/dev/pr31096-pre.html |
+xpog This PR add the particleNet in nano EOY, UL and master. |
This pull request is fully signed and it will be integrated in one of the next master IBs (tests are also fine). This pull request will now be reviewed by the release team before it's merged. @silviodonato, @dpiparo, @qliphy (and backports should be raised in the release meeting by the corresponding L2) |
@hqucms please consider making the backport to 10_6 |
+1 |
PR description:
This PR adds the ParticleNet tagger to NanoAOD. The following scores will be included in NanoAOD:
TvsQCD
,WvsQCD
,ZvsQCD
,HbbvsQCD
,HccvsQCD
,H4qvsQCD
,QCD
Xbb
,Xcc
,Xqq
,QCD
The ParticleNet tagger needs to run in the NanoAOD sequence until we have the tagger stored in MiniAOD. The updated training [V01] should be used for both UL and EOY samples.
FYI @camclean @alefisico