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.
Some of these architectures have already been considered for collider physics, in the context of jet tagging @cite_18 , event topology classification @cite_25 , and for pileup subtraction @cite_28 .
@cite_28 @cite_18 @cite_25
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At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usually referred to as pileup. Pileup mitigation is a key ingredient of the online and offline event reconstruction as pileup affects the reconstruction accuracy of many physics observables. We present a classifier based on Graph Neural Networks, trained to retain particles coming from high-transverse-momentum collisions, while rejecting those coming from pileup collisions. This model is designed as a refinement of the PUPPI algorithm, employed in many LHC data analyses since 2015. Thanks to an extended basis of input information and the learning capabilities of the considered network architecture, we show an improvement in pileup-rejection performances with respect to state-of-the-art solutions. Top-squarks (stops) play a crucial role for the naturalness of supersymmetry (SUSY). However, searching for the stops at the LHC is a tough task especially for some corners of parameter space. To dig the stops out of the huge LHC data, various expert-constructed kinematic variables or cutting-edge analysis techniques have been invented. In this paper, we propose to represent events as graphs and use the message passing neutral network to search for the stops through the process @math at the LHC. We find that the signal and background events can be efficiently discriminated by the patterns of event graphs. Such an approach can thus greatly improve the current LHC sensitivity for the stops.