Machine learning ( ML ) is one of two standard approaches ( together with SED fitting ) for estimating the redshifts of galaxies when only photometric information is available .
ML photo-z solutions have traditionally ignored the morphological information available in galaxy images or partly included it in the form of hand-crafted features , with mixed results .
We train a morphology-aware photometric redshift machine using modern deep learning tools .
It uses a custom architecture that jointly trains on galaxy fluxes , colors and images .
Galaxy-integrated quantities are fed to a Multi-Layer Perceptron ( MLP ) branch while images are fed to a convolutional ( convnet ) branch that can learn relevant morphological features .
This split MLP-convnet architecture , which aims to disentangle strong photometric features from comparatively weak morphological ones , proves important for strong performance : a regular convnet-only architecture , while exposed to all available photometric information in images , delivers comparatively poor performance .
We present a cross-validated MLP-convnet model trained on 130,000 SDSS-DR12 galaxies that outperforms a hyperoptimized Gradient Boosting solution ( hyperopt+XGBoost ) , as well as the equivalent MLP-only architecture , on the redshift bias metric .
The 4-fold cross-validated MLP-convnet model achieves a bias \delta z / ( 1 + z ) = -0.70 \pm 1 \times 10 ^ { -3 } , approaching the performance of a reference ANNZ2 ensemble of 100 distinct models trained on a comparable dataset .
The relative performance of the morphology-aware and morphology-blind models indicates that galaxy morphology does improve ML-based photometric redshift estimation .