{"paper":{"title":"Suppressing the sample variance of DESI-like galaxy clustering with fast simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"A. de la Macorra, A. D. Myers, A. Kremin, A. Lambert, A. Variu, C. Chuang, C. Garcia-Quintero, C. Hahn, C. Howlett, C. Poppett, D. Brooks, D. Sprayberry, E. Gazta\\~naga, E. Mueller, E. Paillas, E. Sanchez, G. Gutierrez, G. Niz, G. Rossi, G. Tarl\\'e, H. Seo, H. Zou, J. Aguilar, J. E. Forero-Romero, J. Mena-Fern\\'andez, J. Nie, J. Silber, K. Fanning, K. Honscheid, L. Le Guillou, M. Landriau, M. Manera, M. Rezaie, M. Schubnell, M. Vargas-Maga\\~na, P. Doel, R. Kehoe, R. Miquel, S. Ahlen, S. Alam, S. Gontcho A Gontcho, S. Juneau, T. Claybaugh, T. Kisner, X. Chen, Y. Yu, Z. Ding","submitted_at":"2024-04-03T23:51:01Z","abstract_excerpt":"Ongoing and upcoming galaxy redshift surveys, such as the Dark Energy Spectroscopic Instrument (DESI) survey, will observe vast regions of sky and a wide range of redshifts. In order to model the observations and address various systematic uncertainties, N-body simulations are routinely adopted, however, the number of large simulations with sufficiently high mass resolution is usually limited by available computing time. Therefore, achieving a simulation volume with the effective statistical errors significantly smaller than those of the observations becomes prohibitively expensive. In this st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.03117","kind":"arxiv","version":3},"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/2404.03117/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"}