Description
In high energy physics we study the fundamental properties of particles by recording their interaction with our detectors. In calorimeters, the particles create showers and deposit energy in the individual calorimeter cells. CMS will build a new calorimeter (HGCAL) with an extremely large number of cells. As these cells are distributed somewhat irregularly, the most natural representation of these energy deposits are point clouds which can represented as graphs. We aim to reconstruct the properties of such events with graph neural networks.
Special Qualifications:
Experience in programming is essential, best in python, experience with neural networks, Linux and object-oriented programming would be useful.
| Field | B2: Data processing (software-oriented) |
|---|---|
| DESY Place | Hamburg |
| DESY Division | FH |
| DESY Group | CMS |
Primary authors
Moritz Scham
(CMS (CMS Fachgruppe Searches))
Dirk Kruecker
(CMS (CMS Fachgruppe Searches))
Isabell Melzer-Pellmann
(CMS (CMS-Experiment))
