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TensorFlow-based DeepDoubleB tagger integration #23033
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A new Pull Request was created by @jmduarte (Javier Duarte) for master. It involves the following packages: DataFormats/BTauReco @perrotta, @monttj, @cmsbuild, @slava77, @gpetruc, @arizzi can you please review it and eventually sign? Thanks. cms-bot commands are listed here |
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@jmduarte |
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Hi @slava77, for submitting a backport to 94X as discussed previously, do we want to keep all the changes (including renameing RecoBTag/DeepFlavour -> RecoBTag/TensorFlow, etc.)? One key difference we will implement is we will disable this in MINIAOD by default (so the code will be there for users to recompute in 94X, but it will not be produced in MINIAOD by default). Thanks, |
On 6/6/18 9:44 AM, Javier Duarte wrote:
Hi @slava77 <https://github.com/slava77>, for submitting a backport to
94X as discussed previously, do we want to keep all the changes
(including renameing RecoBTag/DeepFlavour -> RecoBTag/TensorFlow, etc.)?
One key difference we will implement is we will disable this in MINIAOD
by default (so the code will be there for users to recompute in 94X, but
it will not be produced in MINIAOD by default).
Thanks,
Javier
Hi Javier,
I find it most practical to have a full backport, including the file
renaming.
Will the double-B expected to be used in 94X only for analysis?
|
This adds the DeepDoubleB TensorFlow based model which is based on a lot of the changes brought in from the DeepFlavour pull request #19893. The latest BTV meeting presentation provides more information about the tagger.
We've checked the compatibility between CMSSW-based discriminators computed using the producers in this PR with the output from the training framework (DeepJet) on a ttbar RelVal sample:
MINIAOD vs reference training framework:

https://cernbox.cern.ch/index.php/s/f8bJcJcS8hfU3WF
RECO vs reference training framework:

https://cernbox.cern.ch/index.php/s/hEqbrrMkz5xBSHs
The level of discrepancy is similar to what was observed for the DeepFlavour model, as we also train using MINIAOD inputs.
We also checked that our changes don't affect the existing DeepFlavour model:

https://cernbox.cern.ch/index.php/s/KHs8RKuE0qzREq4
@cvernier @ferencek