Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods

no code implementations • 21 Feb 2022 • Q. Lin, D. Fouchez, J. Pasquet, M. Treyer, R. Ait Ouahmed, S. Arnouts, O. Ilbert

Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent analyses.

Photometric Redshift Estimation Representation Learning

PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys

no code implementations • 24 Mar 2020 • Marko Shuntov, J. Pasquet, S. Arnouts, O. Ilbert, M. Treyer, E. Bertin, S. de la Torre, Y. Dubois, D. Fouchez, K. Kraljic, C. Laigle, C. Pichon, D. Vibert

Combining these PDFs with the density field distribution provides new photometric redshifts, $z_{web}$, whose accuracy is improved by a factor of two (i. e.,${\sigma} \sim 0. 004(1+z)$) for galaxies with $r \leq 17. 8$.

Astrophysics of Galaxies