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HGCal - Minimum Cluster size for Pattern Recognition #27666
HGCal - Minimum Cluster size for Pattern Recognition #27666
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This is to improve robustness of the Pattern Recognition and avoid duplicates
The code-checks are being triggered in jenkins. |
+code-checks Logs: https://cmssdt.cern.ch/SDT/code-checks/cms-sw-PR-27666/11234
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A new Pull Request was created by @felicepantaleo (Felice Pantaleo) for master. It involves the following packages: RecoHGCal/TICL @perrotta, @cmsbuild, @kpedro88, @slava77 can you please review it and eventually sign? Thanks. cms-bot commands are listed here |
please test workflow 20493.52 |
The tests are being triggered in jenkins. |
Comparison job queued. |
Comparison is ready Comparison Summary:
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+upgrade |
@@ -43,6 +43,8 @@ def TICL_iterations_withReco(process): | |||
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process.FilteredLayerClusters = filteredLayerClustersProducer.clone( | |||
clusterFilter = "ClusterFilterByAlgoAndSize", |
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this is already a part of filteredLayerClustersProducer PSet. Is this redundant change really necessary here?
+1 for #27666 b3cbe30
#27666 (review) is minor enough and applies only to an experimental config/customization. |
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. @davidlange6, @slava77, @smuzaffar, @fabiocos (and backports should be raised in the release meeting by the corresponding L2) |
+1 |
PR description:
This PR is introducing a new filter on the minimum layer clusters size that a hgcalLayerCluster requires in order to be passed to the next step of the Pattern Recognition in TICL. This is useful to make PR more robust against single cell inefficiency and to remove duplicate tracksters.
PR validation:
This PR was tested with 20490.0_SingleGammaPt25Eta1p7_2p7 checking that the number of hgcalLayerClusters fed to the pattern recognition is halved, and that the number of duplicate tracksters is decreased.
Another PR will be made in order to make the Pattern Recognition by CA more robust as well.
@rovere @cseez @amartelli fyi