{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-and-accurate-mock-catalogue-generation","title":"Fast and accurate mock catalogue generation for low-mass galaxies","arxiv_id":"1507.05329","date":"2015-07-19","proceeding":null,"authors":["Jun Koda","Chris Blake","Florian Beutler","Eyal Kazin","Felipe Marin"],"abstract":"We present an accurate and fast framework for generating mock catalogues including low-mass halos, based on an implementation of the COmoving Lagrangian Acceleration (COLA) technique. Multiple realisations of mock catalogues are crucial for analyses of large-scale structure, but conventional N-body simulations are too computationally expensive for the production of thousands of realisations. We show that COLA simulations can produce accurate mock catalogues with a moderate computation resource for low- to intermediate- mass galaxies in $10^{12} M_\\odot$ haloes, both in real and redshift space. COLA simulations have accurate peculiar velocities, without systematic errors in the velocity power spectra for k < 0.15 h/Mpc, and with only 3-per-cent error for k < 0.2 h/Mpc. We use COLA with 10 time steps and a Halo Occupation Distribution to produce 600 mock galaxy catalogues of the WiggleZ Dark Energy Survey. Our parallelized code for efficient generation of accurate halo catalogues is publicly available at github.com/junkoda/cola_halo.","url_abs":"https://arxiv.org/abs/1507.05329v1","url_pdf":"https://arxiv.org/pdf/1507.05329v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"fast-and-accurate-mock-catalogue-generation","repo_url":"https://github.com/junkoda/cola_halo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}