Developments since last week

• Working on realistic physics simulations with adapted FST, switiching lowneut=false for faster sim and cleaner environment
  • SIMOPTKEY = G4.physicsmode=3;SimCutParams.lowneut=false;TPCEleParam.doCommonModePerPad=0;TPCEleParam.doIonTailPerPad=1;TPCEleParam.commonModeCoupling=0;TPCEleParam.doNoiseEmptyPads=1;TPCEleParam.doSaturationTail=0;TPCDetParam.TPCRecoWindowSim=10;
  • DIGITOPTKEY = TPCEleParam.doCommonModePerPad=0;TPCEleParam.doIonTailPerPad=1;TPCEleParam.commonModeCoupling=0;TPCEleParam.doNoiseEmptyPads=1;TPCEleParam.doSaturationTail=0;TPCDetParam.TPCRecoWindowSim=10;
• Added QA for ITS-TPC matching
• Added post-training QA after NN training is finished (on training data)
• Tried several different network architectures. Both classification and regression could not majorly benefit from change, fully connected still seems to be the best option so far.
• Worked on fitting class2 regression -> Limited by training data availability: 5 x 50 Ev. @ 50 kHz = ~1700 training data points
• Optimizing training data selection

Post training QA

(Regression, class 1, input size (577))

(Classification, class 1. First: Input size (177), Second: Input size (777))

ITS-TPC matching

(GPU CF: 30.4 mio. clusters, 266.4k tracks; NN: 27.0 mio. clusters, 254.4k tracks)

Interesting effect

We know the network learns a slightly better time-mean position of the clusters and produces a more gaussian shaped width distribution. This seems to result in smaller delta(cog time) cluster-to-track residuals, even though the network was not trained to optimize this (yet)