Morpho-Photometric Redshifts

Abstract

Machine learning (ML) is a standard approach 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.

MNRAS accepted

References in corpus (5)

• Star-galaxy Classification Using Deep Convolutional Neural Networks
• Photometric redshift estimation via deep learning
• Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies
• Morpho-z: improving photometric redshifts with galaxy morphology
• Return of the features. Efficient feature selection and interpretation for photometric redshifts

Cited by in corpus (8)

• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
• 21st Century Statistical and Computational Challenges in Astrophysics
• Eigengalaxies: describing galaxy morphology using principal components in image space
• Photometric Redshift Estimation with Galaxy Morphology using Self-Organizing Maps
• Machine learning applications in astrophysics: Photometric redshift estimation
• Non-Sequential Neural Network for Simultaneous, Consistent Classification and Photometric Redshifts of OTELO Galaxies
• Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning