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.

翻译：准确可靠的测光红移测定是大视场测光巡天的关键环节之一。星系的测光红移测定传统上通过机器学习与人工智能技术解决，这些技术需在同时具备测光与光谱数据的校准星系样本上进行训练。本文提出了一种利用条件生成对抗网络（CGANs）测定星系测光红移的新算法框架。该实现方案能够同时获得测光红移的点估计与概率密度估计。我们使用暗能量巡天（DES）第一年数据对该方法进行了验证，并与混合密度网络（MDN）等现有算法进行了比较。尽管结果显示MDN具有优势，但CGAN的质量评估指标与MDN结果相近，这为CGAN在测光红移估计领域的应用开启了可能性。