Henghes, Ben;
Thiyagalingam, Jeyan;
Pettitt, Connor;
Hey, Tony;
Lahav, Ofer;
(2022)
Deep learning methods for obtaining photometric redshift estimations from images.
Monthly Notices of the Royal Astronomical Society
, 512
(2)
pp. 1696-1709.
10.1093/mnras/stac480.
Preview |
PDF
stac480.pdf - Published Version Download (2MB) | Preview |
Abstract
Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it is impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of particular interest. Here, we investigate different deep learning methods for obtaining photo-z estimates directly from images, comparing these with ‘traditional’ machine learning algorithms which make use of magnitudes retrieved through photometry. As well as testing a convolutional neural network (CNN) and inception-module CNN, we introduce a novel mixed-input model that allows for both images and magnitude data to be used in the same model as a way of further improving the estimated redshifts. We also perform benchmarking as a way of demonstrating the performance and scalability of the different algorithms. The data used in the study comes entirely from the Sloan Digital Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filtre (ugriz) images with complete photometry and a spectroscopic redshift which was taken as the ground truth. The mixed-input inception CNN achieved a mean squared error (MSE) =0.009, which was a significant improvement ( ) over the traditional random forest (RF), and the model performed even better at lower redshifts achieving a MSE = 0.0007 (a improvement over the RF) in the range of z < 0.3. This method could be hugely beneficial to upcoming surveys, such as Euclid and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), which will require vast numbers of photo-z estimates produced as quickly and accurately as possible.
| Type: | Article |
|---|---|
| Title: | Deep learning methods for obtaining photometric redshift estimations from images |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stac480 |
| Publisher version: | http://dx.doi.org/10.1093/mnras/stac480 |
| Language: | English |
| Additional information: | © 2022 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | methods: data analysis, galaxies: distances and redshifts, cosmology: observations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10185817 |
Archive Staff Only
 |
View Item |
