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MXNet-based implementation of the ParticleNet tagger #28902
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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-28902/13689
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A new Pull Request was created by @hqucms (Huilin Qu) for master. It involves the following packages: PhysicsTools/PatAlgos @perrotta, @cmsbuild, @santocch, @slava77 can you please review it and eventually sign? Thanks. cms-bot commands are listed here |
please test with cms-data/RecoBTag-Combined#26. |
@perrotta Looks like the test is not triggered successfully? |
please test with cms-data/RecoBTag-Combined#26 |
The tests are being triggered in jenkins. |
+1 |
Comparison job queued. |
@slava77 |
ok, that's fine then |
Ah, this is quite useful. Thank you!
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+1 |
Comparison job queued. |
Comparison is ready Comparison Summary:
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+1 for #28902 3c9ce7f
Some plots from 1K ttbar events, for pfParticleNetJetTags [ignore the red histos] probQCDothers still seems to have too much at 0 well, perhaps the DiscriminatorsJetTags are more expected to be "naturally" separated these are showing some considerable signal-like fractions. Note: this requires cms-data/RecoBTag-Combined#26 |
to be merged with cms-sw/cmsdist#5626 |
@silviodonato |
merge |
+1 |
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 be automatically merged. |
PR description:
This PR implements the ParticleNet boosted jet tagger into CMSSW. The ParticleNet tagger uses a novel graph neural network architecture and shows significant performance improvement compared to the DeepAK8 algorithm. Two versions are provided: the nominal version (
pfParticleNetJetTags
) is a multi-class tagger for Top, W, Z, Higgs and their various decay modes, with very strong performance but features strong mass sculpting. The mass-decorrelated version (pfMassDecorrelatedParticleNetJetTags
) is a generic two-prong tagger for X->bb, X->cc, and X->qq. It is trained w/ a special signal sample generated with a flat mass spectrum for the signalparticle (X) and shows substantial improvement in both discrimination power and mass decorrelation compared to the mass-decorrelated DeepAK8 tagger. The performance of the ParticleNet tagger is summarized in the DP note (DP-2020/002) and presentations in the JME and the BTV groups.
Needs cms-data/RecoBTag-Combined#26.
The current implementation is based on MXNet. Conversion to ONNX is not successful due to the complexity of the graph neural network architecture. The inference w/ MXNet takes ~60ms per AK8 jet per tagger (nominal and mass-decorrelated), or ~50ms/evt in total on a ttbar sample -- once we solve cms-sw/cmsdist#5528.
PR validation:
Implementation of this PR has been verified with the training framework and shows consistent results.