Learning representations of irregular particle-detector geometry with distance-weighted graph networks
This addresses particle reconstruction for high-energy physics experiments, offering a more flexible and potentially generalizable solution, though it is incremental as it builds on existing graph network approaches.
The paper tackled particle reconstruction in irregular-geometry detectors by introducing GarNet and GravNet graph network layers, which achieved performance comparable to or more resource-efficient than existing methods on a simulated calorimeter dataset.
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex detector geometries. We introduce two distance-weighted graph network architectures, dubbed GarNet and GravNet layers, and apply them to a typical particle reconstruction task. The performance of the new architectures is evaluated on a data set of simulated particle interactions on a toy model of a highly granular calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We study the clustering of energy depositions, which is the basis for calorimetric particle reconstruction, and provide a quantitative comparison to alternative approaches. The proposed algorithms provide an interesting alternative to existing methods, offering equally performing or less resource-demanding solutions with less underlying assumptions on the detector geometry and, consequently, the possibility to generalize to other detectors.