The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning

Creators: Cabayol, L.; Eriksen, M.; Carretero, J.; Casas, R.; Castander, F. J.; Fernández, E.; García-Bellido, J.; Gaztanaga, E.; Hildebrandt, H.; Hoekstra, H.; Joachimi, B.; Miquel, R.; Padilla, C.; Pocino, A.; Sanchez, E.; Serrano, S.; Sevilla, I.; Siudek, M.; Tallada-Crespí, P.; Aghanim, N.; Amara, A.; Auricchio, N.; Baldi, M.; Bender, R.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Douspis, M.; Dubath, F.; Duncan, C. A. J.; Dupac, X.; Dusini, S.; Farrens, S.; Fosalba, P.; Frailis, M.; Franceschi, E.; Franzetti, P.; Garilli, B.; Gillard, W.; Gillis, B.; Giocoli, C.; Grazian, A.; Grupp, F.; Haugan, S. V. H.; Holmes, W.; Hormuth, F.; Hornstrup, A.; Hudelot, P.; Jahnke, K.; Kümmel, M.; Kermiche, S.; Kiessling, A.; Kilbinger, M.; Kohley, R.; Kurki-Suonio, H.; Ligori, S.; Lilje, P. B.; Lloro, I.; Maiorano, E.; Mansutti, O.; Marggraf, O.; Markovic, K.; Marulli, F.; Massey, R.; Mei, S.; Meneghetti, M.; Merlin, E.; Meylan, G.; Moresco, M.; Moscardini, L.; Munari, E.; Nakajima, R.; Niemi, S. M.; Paltani, S.; Pasian, F.; Pedersen, K.; Pettorino, V.; Polenta, G.; Poncet, M.; Popa, L.; Pozzetti, L.; Raison, F.; Rebolo, R.; Rhodes, J.; Riccio, G.; Rosset, C.; Rossetti, E.; Saglia, R.; Sartoris, B.; Schneider, P.; Secroun, A.; Seidel, G.; Sirignano, C.; Sirri, G.; Stanco, L.; Taylor, A. N.; Tereno, I.; Toledo-Moreo, R.; Torradeflot, F.; Tutusaus, I.; Valentijn, E.; Valenziano, L.; Wang, Y.; Weller, J.; Zamorani, G.; Zoubian, J.; Andreon, S.; Scottez, V.; Tramacere, A.; Euclid Consortium

Abstract

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how the synergies between narrow-band photometric data and large imaging surveys can be exploited to improve broadband photometric redshifts. We used a multi-task learning (MTL) network to improve broadband photo-z estimates by simultaneously predicting the broadband photo-z and the narrow-band photometry from the broadband photometry. The narrow-band photometry is only required in the training field, which also enables better photo-z predictions for the galaxies without narrow-band photometry in the wide field. This technique was tested with data from the Physics of the Accelerating Universe Survey (PAUS) in the COSMOS field. We find that the method predicts photo-zs that are 13% more precise down to magnitude iAB < 23; the outlier rate is also 40% lower when compared to the baseline network. Furthermore, MTL reduces the photo-z bias for high-redshift galaxies, improving the redshift distributions for tomographic bins with z > 1. Applying this technique to deeper samples is crucial for future surveys such as Euclid or LSST. For simulated data, training on a sample with iAB z scatter by 16% for all galaxies with iAB zs, which reduces the photo-z scatter by 20% in the COSMOS field.

Additional Information

© The Authors 2023. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is published in open access under the Subscribe to Open model. Subscribe to A&A to support open access publication. This paper is published on behalf of the Euclid Consortium. The PAU Survey is partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509, PID2019-Ш317GB-C31 and Juan de la Cierva fellowship and LACEGAL and EWC Marie Sklodowska-Curie grant No 734374 and no.776247 with ERDF funds from the EU Horizon 2020 Programme, some of which include ERDF funds from the European Union. IEEC and IFAE are partially funded by the CERCA and Beatriu de Pinos program of the Generalitat de Catalunya. Funding for PAUS has also been provided by Durham University (via the ERC StG DEGAS-259586), ETH Zurich, Leiden University (via ERC StG ADULT-279396 and Netherlands Organisation for Scientific Research (NWO) Vici grant 639.043.512), Bochum University (via a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1) as well as an ERC Consolidator Grant (No. 770935)), University College London, Portsmouth support through the Royal Society Wolfson fellowship and from the European Union's Horizon 2020 research and innovation programme under the grant agreement No 776247 EWC. The results published were also funded by the Polish National Agency for Academic Exchange (Bekker grant BPN/BEK/2021/1/00298/DEC/1), the European Union's Horizon 2020 research and innovation programme under the Maria Skłodowska-Curie (grant agreement No 754510) and by the Spanish Ministry of Science and Innovation through Juan de la Cierva-formacion program (reference FJC2018-038792-I). The PAU data centre is hosted by the Port d'Informació Científica (PIC), maintained through a collaboration of CIEMAT and IFAE, with additional support from Universitat Autònoma de Barcelona and ERDF. We acknowledge the PIC services department team for their support and fruitful discussions. CosmoHub has been developed by the Port d'Informació Científica (PIC), maintained through a collaboration of the Institut de Física d'Altes Energies (IFAE) and the Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) and the Institute of Space Sciences (CSIC&IEEC), and was partially funded by the "Plan Estatal de Investigación Científica y Técnica y de Innovación" program of the Spanish government. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the French Centre National d'Etudes Spatiales, the Deutsches Zentrum für Luft- und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Economía y Competitividad, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (http://www.euclid-ec.org). Data availability: The PAUS raw data are publicly available through the ING group. A few reduced images are publicly available at https://www.pausurvey.org. The Flagship catalogue is a property of the Euclid Consortium.

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