{"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/euclid-photometric-redshift-calibration-with","title":"Euclid: Photometric redshift calibration with the clustering redshifts technique","arxiv_id":"2505.10416","date":"2025-05-15","proceeding":null,"authors":["W. d'Assignies","M. Manera","C. Padilla","O. Ilbert","H. Hildebrandt","L. Reynolds","J. Chaves-Montero","A. H. Wright","P. Tallada-Crespí","M. Eriksen","J. Carretero","W. Roster","Y. Kang","K. Naidoo","R. Miquel","B. Altieri","A. Amara","S. Andreon","N. Auricchio","C. Baccigalupi","D. Bagot","M. Baldi","A. Balestra","S. Bardelli","P. Battaglia","A. Biviano","E. Branchini","M. Brescia","S. Camera","V. Capobianco","C. Carbone","V. F. Cardone","S. Casas","F. J. Castander","M. Castellano","G. Castignani","S. Cavuoti","K. C. Chambers","A. Cimatti","C. Colodro-Conde","G. Congedo","C. J. Conselice","L. Conversi","Y. Copin","F. Courbin","H. M. Courtois","M. Crocce","A. Da Silva","H. Degaudenzi","S. de la Torre","G. De Lucia","M. Douspis","X. Dupac","A. Ealet","S. Escoffier","M. Farina","F. Faustini","S. Ferriol","F. Finelli","P. Fosalba","S. Fotopoulou","M. Frailis","E. Franceschi","M. Fumana","S. Galeotta","K. George","B. Gillis","C. Giocoli","P. Gómez-Alvarez","J. Gracia-Carpio","A. Grazian","F. Grupp","W. Holmes","I. M. Hook","A. Hornstrup","K. Jahnke","M. Jhabvala","B. Joachimi","E. Keihänen","S. Kermiche","A. Kiessling","B. Kubik","M. Kümmel","M. Kunz","H. Kurki-Suonio","O. Lahav","A. M. C. Le Brun","S. Ligori","P. B. Lilje","V. Lindholm","I. Lloro","G. Mainetti","D. Maino","E. Maiorano","O. Mansutti","S. Marcin","O. Marggraf","K. Markovic","M. Martinelli","N. Martinet","F. Marulli","R. Massey","D. C. Masters","E. Medinaceli","S. Mei","M. Melchior","Y. Mellier","M. Meneghetti","E. Merlin","G. Meylan","A. Mora","M. Moresco","L. Moscardini","C. Neissner","S. -M. Niemi","S. Paltani","F. Pasian","K. Pedersen","V. Pettorino","S. Pires","G. Polenta","M. Poncet","L. A. Popa","L. Pozzetti","F. Raison","R. Rebolo","A. Renzi","J. Rhodes","G. Riccio","E. Romelli","M. Roncarelli","E. Rossetti","R. Saglia","Z. Sakr","D. Sapone","B. Sartoris","J. A. Schewtschenko","P. Schneider","T. Schrabback","A. Secroun","E. Sefusatti","G. Seidel","M. Seiffert","S. Serrano","P. Simon","C. Sirignano","G. Sirri","A. Spurio Mancini","L. Stanco","J. Steinwagner","D. Tavagnacco","A. N. Taylor","H. I. Teplitz","I. Tereno","N. Tessore","S. Toft","R. Toledo-Moreo","F. Torradeflot","A. Tsyganov","I. Tutusaus","L. Valenziano","J. Valiviita","T. Vassallo","G. Verdoes Kleijn","Y. Wang","J. Weller","G. Zamorani","E. Zucca","M. Bolzonella","C. Burigana","L. Gabarra","J. Martín-Fleitas","I. Risso","V. Scottez","M. Viel"],"abstract":"Aims: The precision of cosmological constraints from imaging surveys hinges on accurately estimating the redshift distribution $ n(z) $ of tomographic bins, especially their mean redshifts. We assess the effectiveness of the clustering redshifts technique in constraining Euclid tomographic redshift bins to meet the target uncertainty of $ \\sigma ( \\langle z \\rangle ) < 0.002 (1 + z) $. In this work, these mean redshifts are inferred from the small-scale angular clustering of Euclid galaxies, which are distributed into bins with spectroscopic samples localised in narrow redshift slices. Methods: We generate spectroscopic mocks from the Flagship2 simulation for the Baryon Oscillation Spectroscopic Survey (BOSS), the Dark Energy Spectroscopic Instrument (DESI), and Euclid's Near-Infrared Spectrometer and Photometer (NISP) spectroscopic survey. We evaluate and optimise the clustering redshifts pipeline, introducing a new method for measuring photometric galaxy bias (clustering), which is the primary limitation of this technique. Results: We have successfully constrained the means and standard deviations of the redshift distributions for all of the tomographic bins (with a maximum photometric redshift of 1.6), achieving precision beyond the required thresholds. We have identified the main sources of bias, particularly the impact of the 1-halo galaxy distribution, which imposed a minimal separation scale of 1.5 Mpc for evaluating cross-correlations. These results demonstrate the potential of clustering redshifts to meet the precision requirements for Euclid, and we highlight several avenues for future improvements.","url_abs":"https://arxiv.org/abs/2505.10416v1","url_pdf":"https://arxiv.org/pdf/2505.10416v1.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":"euclid-photometric-redshift-calibration-with","repo_url":"https://github.com/wdoumerg/Clustering_z","is_official":1,"mentioned_in_paper":0,"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}