{"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/photometric-redshift-error-estimators","title":"Photometric Redshift Error Estimators","arxiv_id":"0711.0962","date":"2007-11-06","proceeding":null,"authors":["Hiroaki Oyaizu","Marcos Lima","Carlos E. Cunha","Huan Lin","Joshua Frieman"],"abstract":"Photometric redshift (photo-z) estimates are playing an increasingly\nimportant role in extragalactic astronomy and cosmology. Crucial to many\nphoto-z applications is the accurate quantification of photometric redshift\nerrors and their distributions, including identification of likely catastrophic\nfailures in photo-z estimates. We consider several methods of estimating\nphoto-z errors and propose new training-set based error estimators based on\nspectroscopic training set data. Using data from the Sloan Digital Sky Survey\nand simulations of the Dark Energy Survey as examples, we show that this method\nprovides a robust, relatively unbiased estimate of photo-z errors. We show that\nculling objects with large, accurately estimated photo-z errors from a sample\ncan reduce the incidence of catastrophic photo-z failures.","url_abs":"http://arxiv.org/abs/0711.0962v1","url_pdf":"http://arxiv.org/pdf/0711.0962v1.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":"abstracts"},"code_links":[{"paper_slug":"photometric-redshift-error-estimators","repo_url":"https://github.com/IftachSadeh/ANNZ","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"astronomy","task_name":"Astronomy"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}