MMLPhoto-z: Cross-modal contrastive learning method for estimating photo-z of quasars

MMLPhoto-z estimates the photo-z of quasars using a cross-modal contrastive learning approach. This method employs adversarial training and contrastive loss functions to promote the mutual conversion between multi-band photometric data features (magnitude, color) and photometric image features, while extracting modality-invariant features. MMLPhoto-z can also be applied to tasks like photo-z estimation for galaxies with missing magnitudes. Overall, this method proves effective in enhancing the photo-z estimation across diverse datasets and conditions.