{"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/class-sz-i-overview","title":"class_sz I: Overview","arxiv_id":"2310.18482","date":"2023-10-27","proceeding":null,"authors":["B. Bolliet","A. Kusiak","F. McCarthy","A. Sabyr","K. Surrao","J. C. Hill","J. Chluba","S. Ferraro","B. Hadzhiyska","D. Han","J. F. Macías-Pérez","M. Madhavacheril","A. Maniyar","Y. Mehta","S. Pandey","E. Schaan","B. Sherwin","A. Spurio Mancini","Í. Zubeldia"],"abstract":"class_sz is a versatile and robust code in C and Python that can compute theoretical predictions for a wide range of observables relevant to cross-survey science in the Stage IV era. The code is public at https://github.com/CLASS-SZ/class_sz along with a series of tutorial notebooks (https://github.com/CLASS-SZ/notebooks). It will be presented in full detail in paper II. Here we give a brief overview of key features and usage.","url_abs":"https://arxiv.org/abs/2310.18482v1","url_pdf":"https://arxiv.org/pdf/2310.18482v1.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":"class-sz-i-overview","repo_url":"https://github.com/class-sz/class_sz","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"class-sz-i-overview","repo_url":"https://github.com/class-sz/notebooks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}