The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning
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 i_{AB} < 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 i_{AB} <23, the method reduces the photo-z scatter by 16% for all galaxies with i_{AB}<25. We also studied the effects of extending the training sample with photometric galaxies using PAUS high-precision photo-zs, which reduces the photo-z scatter by 20% in the COSMOS field.
Keywords
Cite
@article{arxiv.2209.10161,
title = {The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning},
author = {L. Cabayol and M. Eriksen and J. Carretero and R. Casas and F. J. Castander and E. Fernández and J. Garcia-Bellido and E. Gaztanaga and H. Hildebrandt and H. Hoekstra and B. Joachimi and R. Miquel and C. Padilla and A. Pocino and E. Sanchez and S. Serrano and I. Sevilla and M. Siudek and P. Tallada-Crespí and N. Aghanim and A. Amara and N. Auricchio and M. Baldi and R. Bender and D. Bonino and E. Branchini and M. Brescia and J. Brinchmann and S. Camera and V. Capobianco and C. Carbone and M. Castellano and S. Cavuoti and A. Cimatti and R. Cledassou and G. Congedo and C. J. Conselice and L. Conversi and Y. Copin and L. Corcione and F. Courbin and M. Cropper and A. Da Silva and H. Degaudenzi and M. Douspis and F. Dubath and C. A. J. Duncan and X. Dupac and S. Dusini and S. Farrens and P. Fosalba and M. Frailis and E. Franceschi and P. Franzetti and B. Garilli and W. Gillard and B. Gillis and C. Giocoli and A. Grazian and F. Grupp and S. V. H. Haugan and W. Holmes and F. Hormuth and A. Hornstrup and P. Hudelot and K. Jahnke and M. Kümme and S. Kermiche and A. Kiessling and M. Kilbinger and R. Kohley and H. Kurki-Suonio and S. Ligori and P. B. Lilje and I. Lloro and E. Maiorano and O. Mansutti and O. Marggraf and K. Markovic and F. Marulli and R. Massey and M. Meneghetti and E. Merlin and G. Meylan and M. Moresco and L. Moscardini and E. Munari and R. Nakajima and S. M. Niemi and S. Paltani and F. Pasian and K. Pedersen and V. Pettorino and G. Polenta and M. Poncet and L. Popa and L. Pozzetti and F. Raison and R. Rebolo and J. Rhodes and G. Riccio and C. Rosset and E. Rossetti and R. Saglia and B. Sartoris and P. Schneider and A. Secroun and G. Seide and C. Sirignano and G. Sirri and L. Stanco and A. N. Taylor and I. Tereno and R. Toledo-Moreo and F. Torradeflot and I. Tutusaus and E. Valentijn and L. Valenziano and Y. Wang and J. Weller and G. Zamorani and J. Zoubian and S. Andreon and S. Mei and V. Scottez and A. Tramacere},
journal= {arXiv preprint arXiv:2209.10161},
year = {2023}
}
Comments
20 pages, 16 figures