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Fully implement fishbone in patatrack #36215
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I see, I'm guessing that it's because of #36167 |
-1 Summary: https://cmssdt.cern.ch/SDT/jenkins-artifacts/pull-request-integration/PR-bed46a/20882/summary.html Comparison SummarySummary:
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+heterogeneous |
+1 |
Does geometry require more info? |
+1 |
This pull request is fully signed and it will be integrated in one of the next master IBs (but tests are reportedly failing). This pull request will now be reviewed by the release team before it's merged. @perrotta, @dpiparo, @qliphy (and backports should be raised in the release meeting by the corresponding L2) |
Differences only show up in the GPU workflow 11634.506 (2021 TTbar_14TeV Patatrack_PixelOnlyTripletsGPU) Could anyone please remind me what that workflow is intended to? Is it a workflow meant to test some "development" work in GPU? Is it eventually foreseen a CPU counterpart of that code (or maybe it exists already, but it is not tested in a workflow)? |
@perrotta this is fully expected. |
Grazie @VinInn |
@perrotta , no idea. Ask trigger coordination. I do not think that we have an official Run3 HLT menu yet. |
+1 |
merge |
Hi Andrea,
The Patatrack pixel reconstruction is used in two ways in the
As for Vincenzo's comment
that is correct: the first draft of the Run-3 menu will be the outcome of the Trigger Reviews that are going on this week, and there's very little in release for the time being. Specifically for the pixel and full tracking, the plan is
Once that is done, all workflows should use the new pixel reconstruction by default for the HLT, and |
I eventually fully implemented the fishbone algorithm.
The main differences w/r/t previous code are
when the fishbone identifies 2 collinear doublets between the same layers it chooses the longest and store the intermediate hit
Those "new" hits are added to the track (max 2)
Fit is performed for all hits up to 6 (the hits are chosen to maximize lever arm)
all selections uses number of layers, not number of hits
from the MTV below one can observe that
efficiency is not affected: even improves
there is a clear reduction of fakes
resolution dramatically improves
vertexing is not affected
My conclusion is that Physics performance improved.
MTV
ttbar: http://innocent.home.cern.ch/innocent/RelVal/gpuMTV122fbnew/
single muon ideal: http://innocent.home.cern.ch/innocent/RelVal/SingleMuFlatIdeal_gpu122fbnew/
This PR focuses on Physics performance. Once settled, computational performance and code cleaning can be addressed in a purely technical PR.
expect regression!
p.s. Some technicality needed to be improve anyhow
Slides for TRK-POG in https://indico.cern.ch/event/1098365/#3-new-fishbone