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Liquid Argon Time Projection Chambers (LArTPCs) are a class of detectors thatproduce high resolution images of charged particles within their sensitivevolume. In these images, the clustering of distinct particles intosuperstructures is of central importance to the current and future neutrinophysics program. Electromagnetic (EM) activity typically exhibits spatiallydetached fragments of varying morphology and orientation that are challengingto efficiently assemble using traditional algorithms. Similarly, particles thatare spatially removed from each other in the detector may originate from acommon interaction. Graph Neural Networks (GNNs) were developed in recent yearsto find correlations between objects embedded in an arbitrary space. The GraphParticle Aggregator (GrapPA) first leverages GNNs to predict the adjacencymatrix of EM shower fragments and to identify the origin of showers, i.e.primary fragments. On the PILArNet public LArTPC simulation dataset, thealgorithm achieves achieves a shower clustering accuracy characterized by amean adjusted Rand index (ARI) of 97.8 % and a primary identification accuracyof 99.8 %. It yields a relative shower energy resolution of $(4.1+1.4/\sqrt{E(\text{GeV})})\,\%$ and a shower direction resolution of$(2.1/\sqrt{E(\text{GeV})})^{\circ}$. The optimized algorithm is then appliedto the related task of clustering particle instances into interactions andyields a mean ARI of 99.2 % for an interaction density of$\sim\mathcal{O}(1)\,m^{-3}$.

Clustering of Electromagnetic Showers and Particle Interactions with Graph Neural Networks in Liquid Argon Time Projection Chambers Data