PhotoRedshift-MML: A multimodal machine learning method for estimating photometric redshifts of quasars

Abstract

ABSTRACT We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415 930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |Δz| = |(zphot − zspec)/(1 + zspec)| to evaluate the redshift prediction and demonstrated a $4.04{{\ \rm per\ cent}}$ increase in accuracy. With the help of the generated spectral features, the proportion of data with |Δz| < 0.1 can reach $84.45{{\ \rm per\ cent}}$ of the total test samples, whereas it reaches $80.41{{\ \rm per\ cent}}$ for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |Δz| is shown to decrease from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction.