{"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/a-new-census-of-the-0-2-z-3-0-universe-part","title":"A New Census of the 0.2< z <3.0 Universe, Part II: The Star-Forming Sequence","arxiv_id":"2110.04314","date":"2021-10-08","proceeding":null,"authors":["Joel Leja","Joshua S. Speagle","Yuan-Sen Ting","Benjamin D. Johnson","Charlie Conroy","Katherine E. Whitaker","Erica J. Nelson","Pieter van Dokkum","Marijn Franx"],"abstract":"We use the panchromatic SED-fitting code Prospector to measure the galaxy logM$^*$-logSFR relationship (the `star-forming sequence') across $0.2 < z < 3.0$ using the COSMOS-2015 and 3D-HST UV-IR photometric catalogs. We demonstrate that the chosen method of identifying star-forming galaxies introduces a systematic uncertainty in the inferred normalization and width of the star-forming sequence, peaking for massive galaxies at $\\sim 0.5$ dex and $\\sim0.2$ dex respectively. To avoid this systematic, we instead parameterize the density of the full galaxy population in the logM$^*$-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization $0.2-0.5$ dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from spectral energy distribution fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing $\\sim0.2-0.5$ dex offset with observations at $0.5<z<3$. The fully trained normalizing flow including a nonparametric description of $\\rho(\\log{\\rm M}^*,\\log{\\rm SFR},z)$ is made available online to facilitate straightforward comparisons with future work.","url_abs":"https://arxiv.org/abs/2110.04314v2","url_pdf":"https://arxiv.org/pdf/2110.04314v2.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":"a-new-census-of-the-0-2-z-3-0-universe-part","repo_url":"https://github.com/jrleja/sfs_leja_trained_flow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2110.04314","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}