{"paper":{"title":"ML for the hKLM at the 2nd Detector","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"Graph neural networks outperform classical methods for energy measurement and particle identification in a simulated iron-scintillator calorimeter for the Electron Ion Collider.","cross_cats":["hep-ex"],"primary_cat":"physics.ins-det","authors_text":"Anselm Vossen, Rowan Kelleher","submitted_at":"2026-04-09T16:42:51Z","abstract_excerpt":"The present research applies Graph Neural-Networks (GNNs) for energy measurement and particle identification tasks for a proposed second detector at the future Electron Ion Collider (EIC). In particular, an iron-scintillator sampling calorimeter would provide neutral hadron ($K_L$ and neutron) energy measurements and identification, as well as separation of muons from hadrons. Using detector simulations, particle hits in the detector are represented as graphs, and a GNN is trained for either classification or prediction. Furthermore, we developed a parameterization of the scintillator optical "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We find that the GNN method outperforms classical methods at the same tasks, and we report projections for the energy and timing resolution, and identification accuracy of the calorimeter.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That performance metrics measured on simulated events will translate to real data without large unmodeled systematics in the iron-scintillator response or in the GNN generalization.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Graph neural networks trained on simulated hits outperform classical methods for energy resolution, timing, and particle identification in an iron-scintillator sampling calorimeter, with an integrated multi-objective optimization framework for design tradeoffs.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Graph neural networks outperform classical methods for energy measurement and particle identification in a simulated iron-scintillator calorimeter for the Electron Ion Collider.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d9187e97e2a271944ddecbf3773c23769dac22b4e50e660536a4444f2973c6cd"},"source":{"id":"2604.08447","kind":"arxiv","version":2},"verdict":{"id":"ea0ec276-66c7-450f-b822-309989b7e731","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T17:35:31.211794Z","strongest_claim":"We find that the GNN method outperforms classical methods at the same tasks, and we report projections for the energy and timing resolution, and identification accuracy of the calorimeter.","one_line_summary":"Graph neural networks trained on simulated hits outperform classical methods for energy resolution, timing, and particle identification in an iron-scintillator sampling calorimeter, with an integrated multi-objective optimization framework for design tradeoffs.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That performance metrics measured on simulated events will translate to real data without large unmodeled systematics in the iron-scintillator response or in the GNN generalization.","pith_extraction_headline":"Graph neural networks outperform classical methods for energy measurement and particle identification in a simulated iron-scintillator calorimeter for the Electron Ion Collider."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.08447/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}