Speaker
Maurizio Pierini
(CERN)
Description
We use Graph Networks to learn representations of irregular detector geometries and perform on it typical tasks such as cluster segmentation or pattern recognition. Thanks to the flexibility and generality of the graph architecture, this kind of network can be applied to detector of arbitrarly geometry, representing the detector elements through a unique detector identification (e.g., physical position) and the readout value and embedding as vertices in a graph. We apply this idea to tasks related to calorimetry and tracking in LHC-like conditions, investigating original graph architectures to optimise performance and memory footprint.
| Consider for promotion | Yes |
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Primary authors
Mauro Verzetti
(CERN)
Maurizio Pierini
(CERN)
Jan Kieseler
(CERN)
Swapneel Sundeep Mehta
(Dwarkadas J Sanghvi College of Engineering (IN))
Shah Rukh Qasim
(SEECS - School of Electrical Engine ering and Computer Science )
Yutaro Iiyama
(CERN)
