Multi-class classification

• Adapted training data selection for multi-class classification
  • Try to predict class 0 (reject), class 1 (accept, don't split), class 2 (accept and split)
  • Try to split digit maxima which belong to two real tracks, but only predict class 1 if e.g. track + high incl. track (e.g. looper) create cluster 
• Both classification and regression data adjusted, but facing some limitations:
  • After downsampling from 5 x 50kHz, PbPb simulations I got ~3000 class 2 points
• Model seems to find more split clusters at smaller radii, but also sometimes splits clusters which shouldn't be split

Area overlap study

• Simple way of removing loopers: Make cut on the maximum area of a cluster with same MC label (if area < 200 -> accept)
• Now the trending is gone, interestingly

• Compare to GPU CF

Plans & To-Do's

Title and text; High priority, medium priority, low priority; short term, mid term, long term; other

• Neural networks
  • Cluster splitter network
    • (✓) N-class classifier network: Probably only going until split-level 2 or 3. Higher gets really sparse in training data
    • (✓) N-class regression network: Similar approach as the N-class classifier, but need to see how good performance is...
    • Pass momentum vector to downstream reconstruction
• GPU developments
  • Pull requests
    • O2
      • PR, ORT library integration: https://github.com/AliceO2Group/AliceO2/pull/13522 
      • PR, Full clusterization integration: https://github.com/AliceO2Group/AliceO2/pull/13610
    • alidist
      • PR, ORT GPU build: https://github.com/alisw/alidist/pull/5622
  • Issues & Feature requests
    • OnnxRuntime
      • Coming up...
• QA task & algorithmic developments
  • Include SC distortion simulation
  • Use black PbPb data to evaluate performance and for training
  • Redo 2D study with NN