Table of Contents
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Useful links
CP groups
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- JETM2 non-allhadronic ttbar mc20 (JIRA)
Planning
Ongoing studies
- Understanding bias in JES in low truth-jet pT
- Truth-jet matching (Brendon)
- Check different truth jet matching schemes for response definition
- Performance as a function of dR quality cut
- Check different truth jet matching schemes for response definition
- Evaluating the impact of soft lepton information (Prajita)
- Performance with LeptonID
- Apply additional selection on soft electron
- Auxiliary tasks (Brendon)
- Fixing weights of TruthOrigin classes
- Add TruthParticleID
- Model architecture (Brendon)
- Attention mechanisms
- Hyperparameter optimization
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Samples and models on SDF
TDD output files:
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# ttbar non-all hadronic sample in PHYSVAL format; 600 GB /gpfs/slac/atlas/fs1/d/pbhattar/BjetRegression/Input_Ftag_Ntuples/nonallhad_PHYSVAL_mc20_410470 # ttbar samples in FTAG format; 900 GB /gpfs/slac/atlas/fs1/d/pbhattar/BjetRegression/Input_Ftag_Ntuples/Rel22_ttbar_DiLep /gpfs/slac/atlas/fs1/d/pbhattar/BjetRegression/Input_Ftag_Ntuples/Rel22_ttbar_SingleLep /gpfs/slac/atlas/fs1/d/pbhattar/BjetRegression/Input_Ftag_Ntuples/Rel22_ttbar_AllHadronic |
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### Trained on UPP_1 # No auxiliary tasks /gpfs/slac/atlas/fs1/d/bbullard/BjetRegression/models/GNX-bjr-ftag1_1p5mil_NoAux_21param_20230918-T082841 /gpfs/slac/atlas/fs1/d/bbullard/BjetRegression/models/GNX-bjr-ftag1_1p5mil_NoAux_21param_20230918-T174839 ### Trained on UPP_2 # No auxiliary tasks, trained in 2 separate batches /gpfs/slac/atlas/fs1/d/bbullard/BjetRegression/models/GNX-bjr-physval_80mil_NoAux_21param_v1_20231013-T121459 /gpfs/slac/atlas/fs1/d/bbullard/BjetRegression/models/GNX-bjr-physval_80mil_NoAux_21param_v2_20231013-T121459 # With auxiliary tasks /gpfs/slac/atlas/fs1/d/bbullard/BjetRegression/models/GNX-bjr-physval_80mil_Aux_21param_20231023-T080935 ### Trained on UPP_3 /gpfs/slac/atlas/fs1/d/bbullard/BjetRegression/models/GNX-bjr-physval_80mil_NoAux_21param_PtResampled |
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SDF preliminaries
Compute resources
SDF has a shared queue for submitting jobs via slurm, but this partition has extremely low priority. Instead, use the usatlas
partition or request Michael Kagan to join the atlas
partition.
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/gpfs/slac/atlas/fs1/d/bbullard/conda_env_yaml/ ├── puma.yaml ├── salt.yaml └── upp.yaml |
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Producing H5 samples with Training Dataset Dumper
Introduction
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Overview (FTag Workshop Sept. 2023): presentation by Nikita Pond
Tutorial: TDD on FTag docs
General introduction to Athena in Run 3 (component accumulator): ATLAS Software Docs
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base_sdf_dir/nonallhad_PHYSVAL_mc20_410470 This was processed on mc20 all hadronic ttbar samples, to be replaced by mc21 or mc23 13p6 samples.
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Plotting with Umami/Puma (Work In Progress)
NOTE: we have not yet used umami/puma for plotting or preprocessing, but it can in principle be done and we will probably try to get this going soon.
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This took quite some time to run, so (again) save yourself the effort and use the precompiled environments.
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Pre-processing
SALT likes to take preprocesses data file formats from Umami (though in principle the format is the same as what's produced by the training dataset dumper).
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- within the FTag software, there exist flavor classifications (for example lquarkjets, bjets, cjets, taujets, etc). These can be used to define different sample components. Further selections
based on kinematic cuts can be made through theregion
key. - It appears only 2 features can be specified for the resampling (nominally pt and eta).
- The binning also appears not to be respected for the variables used in resampling.
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Model development with SALT
The slac-bjr git project contains a fork of SALT. One can follow the SALT documentation for general installation/usage. Some specific notes can be found below:
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To do: develop this, and add some notes on it (Brendon/Prajita)
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Miscellaneous tips
You can grant read/write access for GPFS data folder directories to ATLAS group members via the following (note that this does not work for SDF home folder)
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