Ongoing activites

• Looper tagging & pad-parallel tagging
• Retrain NN's to exclude loopers
• pT & η differential studies

1. Looper tagging

• Both looper tagging and pad-parallel tagging work
• Same algorithm, different "cuts" -> In process of optimization (fewest possible rejected volume with maximum possible fake reduction)
• Total rejected TPC volume ~15-16%

• Weakness for larger loopers -> needs fixing with applied cuts

2. p_T & η differential studies

• Checked performance with and without looper tagging
• Results make sense and show that the network get's more "confused" with loopers

3. Retraining NN's

• Next step in the pipeline
• Need to check that training data is read out correctly (some changes were necessary in the QA task)

4. Visualization & PyTorch

• Found a nice GitHub repo with some latex code for plotting neural networks

• Pytorch forums: Opened thread on Conv3D not utilizing tensor cores - https://discuss.pytorch.org/t/conv3d-tensor-core-utilisation/192389

Minutes from private discussion

1. Looper tagger

• Only use pad-parallel tagging
• Only mask regions around individual clusters, not full regions of investigation
• Re-branding of looper tagger -> "Exclusion of identified regions from efficiency calculation"
• Re-branding of pad-parallel tracks -> tracks with high inclination angle

2. Cluster writing

• Create own tpc-native-clusters.root to run tracking and create tracking QA once running

3. Neural network

• Try with only fully connected layers