{"paper":{"title":"On the accuracy of dark matter halo merger trees and the consequences for semi-analytic models of galaxy formation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.GA","authors_text":"2), (2) ARC Centre for All-Sky Astrophysics in 3 Dimensions (ASTRO 3D)), \\'Angel Chandro-G\\'omez (1, Australia, Cedric G. Lacey, Chris Power, Claudia del P. Lagos, Crawley, John C. Helly, Joop Schaye ((1) International Centre for Radio Astronomy Research (ICRAR), Matthieu Schaller, Robert J. McGibbon, The University of Western Australia, Victor J. Forouhar Moreno, WA","submitted_at":"2025-01-13T20:26:25Z","abstract_excerpt":"Galaxy formation and evolution models, such as semi-analytic models, are powerful theoretical tools for predicting how galaxies evolve across cosmic time. These models follow the evolution of galaxies based on the halo assembly histories inferred from large $N$-body cosmological simulations. This process requires codes to identify halos (\"halo finder\") and to track their time evolution (\"tree builder\"). While these codes generally perform well, they encounter numerical issues when handling dense environments. In this paper, we present how relevant these issues are in state-of-the-art cosmologi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.07677","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.07677/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"}