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

TL;DR

This paper introduces a multimodal machine learning approach that significantly improves the accuracy of photometric redshift estimation for quasars by leveraging spectral features learned from photometric data, outperforming single-modal methods.

Contribution

The paper presents a novel multimodal learning framework combining feature transformation and transfer learning to enhance quasar redshift predictions from photometric data.

Findings

Achieved a 4.04% increase in prediction accuracy.

84.45% of test samples had |Δz|<0.1 with the proposed method.

Reduced RMS of |Δz| from 0.1332 to 0.1235.

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 |{\Delta}z| = |(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and demonstrated a 4.04% increase in accuracy. With the help of the generated spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach 84.45% of the total test samples, whereas it reaches 80.41% for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown to decreases 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. The algorithm code can be found at https://github.com/HongShuxin/PhotoRedshift-MML .