[ ] Typical NN classifier: A NN that classifies bins as good or bad:
Input features: max number of hits times (x,y,z), N hidden layers, output node: [0,1] probability, [0,1] contamination
[ ] Typical NN classier:
Take first NN with only [0,1] probability output, classify every hit in the bin to be on/off.
Best architecture would be an RNN.
[ ] Inference NN:
Let's assume your HT brings hight quality track candidates (start with optimal):
Construct a NN that predict the particle properties of the particle that created the hits.
Input would be:
Maximum size x (x,y,z) -> Some hidden layers -> 6 variables: (x0,y0,z0),(px0,py0,pz0).
Input features: max number of hits times (x,y,z), N hidden layers, output node: [0,1] probability, [0,1] contamination
Take first NN with only [0,1] probability output, classify every hit in the bin to be on/off. Best architecture would be an RNN.
Let's assume your HT brings hight quality track candidates (start with optimal): Construct a NN that predict the particle properties of the particle that created the hits.
Input would be: Maximum size x (x,y,z) -> Some hidden layers -> 6 variables: (x0,y0,z0),(px0,py0,pz0).