Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)
This addresses photometric redshift estimation for wide-field surveys, but it is incremental as it shows CGANs are competitive with existing methods like MDN.
The paper tackled photometric redshift determination for galaxies using Conditional Generative Adversarial Networks (CGANs), achieving results close to a Mixture Density Network (MDN) on Dark Energy Survey Y1 data, with CGANs providing both point and probability-density estimates.
Accurate and reliable photometric redshift determination is one of the key aspects for wide-field photometric surveys. Determination of photometric redshift for galaxies, has been traditionally solved by use of machine-learning and artificial intelligence techniques trained on a calibration sample of galaxies, where both photometry and spectrometry are available. On this paper, we present a new algorithmic approach for determining photometric redshifts of galaxies using Conditional Generative Adversarial Networks (CGANs). The proposed implementation is able to determine both point-estimation and probability-density estimations for photometric redshifts. The methodology is tested with data from Dark Energy Survey (DES) Y1 data and compared with other existing algorithm such as a Mixture Density Network (MDN). Although results obtained show a superiority of MDN, CGAN quality-metrics are close to the MDN results, opening the door to the use of CGAN at photometric redshift estimation.