[2603.23356] Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors

High Energy Physics - Experiment arXiv:2603.23356 (hep-ex) [Submitted on 24 Mar 2026] Title:Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors Authors:Max Marriott-Clarke, Lazar Novakovic, Elizabeth Ratzer, Robert J. Bainbridge, Loukas Gouskos, Benedikt Maier View a PDF of the paper titled Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors, by Max Marriott-Clarke and 5 other authors View PDF HTML (experimental) Abstract:We propose a novel clustering approach for point-cloud segmentation based on supervised contrastive metric learning (CML). Rather than predicting cluster assignments or object-centric variables, the method learns a latent representation in which points belonging to the same object are embedded nearby while unrelated points are separated. Clusters are then reconstructed using a density-based readout in the learned metric space, decoupling representation learning from cluster formation and enabling flexible inference. The approach is evaluated on simulated data from a highly granular calorimeter, where the task is to separate highly overlapping particle showers represented as sets of calorimeter hits. A direct comparison with object condensation (OC) is performed using identical graph neural network backbones and equal latent dimensionality, isolating the effect of the learning objective. The CML method produces a more stable and separable embedding geometry for both electromagnetic and had...