Oct 19 – 23, 2020
Europe/Zurich timezone

Improving particle-flow with deep learning

Oct 23, 2020, 10:35 AM
20m
Regular talk 1 ML for data reduction : Application of Machine Learning to data reduction, reconstruction, building/tagging of intermediate object Workshop

Speaker

Sanmay Ganguly (Weizmann Institute of Science (IL))

Description

Canonical particle flow algorithm tries to estimate neutral energy deposition in calorimeter by first performing matching between calorimeter deposits and track
direction and subsequently subtracting the track momenta from the matched cluster energy deposition.
We propose a Deep Learning based method for estimating the energy fraction of individual components for each cell of the calorimeter.
We build the dataset by a toy detector (with different resolutions per calorimeter layer) using GEANT and apply image-based deep neural network models to regress the fraction of neutral energy per cell of the
detector. A comparison of the performance of several different models is carried out.

Primary author

Sanmay Ganguly (Weizmann Institute of Science (IL))

Co-authors

Jonathan Shlomi (Weizmann Institute of Science (IL)) Eilam Gross (Weizmann Institute of Science (IL)) Marumi Kado (Sapienza Universita e INFN, Roma I (IT)) Francesco Armando Di Bello (Sapienza Universita e INFN, Roma I (IT))

Presentation materials