Speaker
Tommaso Dorigo
(Universita e INFN, Padova (IT))
Description
We develop a nearest neighbor algorithm for regressor for the problem of estimating the energy of multi-TeV muons in a high-granularity calorimeter, exploiting the pattern of soft photon deposits around the muon track. The algorithm is heavily overparametrized by assigning weights and biases to the training events. Parameters are learnt by batch gradient descent. The performance compares favourably with that of xgdboost and a neural network, although CPU consumption is orders of magnitude larger.
Primary author
Tommaso Dorigo
(Universita e INFN, Padova (IT))
