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Train ECal ParticleNet on non-fiducial events to determine whether this subset is problematic for an ECal-only ParticleNet model. We are planning to have two people work on this project and compare results to cross-validate
Tasks
Skim events with a cut that removes all fiducial events (my current thinking is to use a shared directory of processed files and make the additional fiducial cut in dataset.py instead of file_processor.py)
Train and Evaluate ParticleNet on these events
Plot results (ROC curves and pT bias)
Existing material to start off
A description of fiducial events and some example code can be found at Issue#27
You can take a look at some v14 ParticleNet code but keep in mind this is in development and some of it includes HCal information
I will organize a more succinct directory with stable v14 ECal ParticleNet code. Once that is done I will merge it with IncandelaLab LDMX-scripts and update this issue with a link to the new directory
The text was updated successfully, but these errors were encountered:
Missing cellmodule.txt for v14 (LDMX-scripts/GraphNet/data/v14), which is needed when comparing ECal face projection of incoming particle and the ECal cells position. Did cellmodule.txt changed for v13 and v14? May I use the v12 cell geometry for non-fiducial study?
Issue description
Train ECal ParticleNet on non-fiducial events to determine whether this subset is problematic for an ECal-only ParticleNet model. We are planning to have two people work on this project and compare results to cross-validate
Tasks
Existing material to start off
The text was updated successfully, but these errors were encountered: