Morpho-photometric redshifts

Abstract

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 δz/(1+z) = −0.70±1×10 −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.

Nonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies

“…The galaxy colors are related to both the redshift and the morphology. However, the contradictory results obtained by other researchers for and against including morphological data to increase the redshift estimate accuracy (Tagliaferri et al 2003;Singal et al 2011;Menou 2019;Wilson et al 2020) suggest that the morphological properties provide limited information to attain this goal. Therefore, we used bootstrap resampling to analyze the effect of the galaxy classification probabilities passed onto the redshift module in our neural network model.…”

“…In contrast, Tagliaferri et al (2003) include photometry, surface brightnesses, and Petrosian radii and fluxes, reducing the errors by a factor of about 30%. More recently, Menou (2019) presents a nonsequential network with two inputs and one output, a convolutional branch to analyze images, and a branch composed of a dense set of layers to deal with the photometric data, reporting an improvement in the redshift estimates. For our redshift analysis, we take into account the probability yielded by the Classification module.…”

Non-Sequential Neural Network for Simultaneous, Consistent Classification and Photometric Redshifts of OTELO Galaxies

“…The galaxy colors are related to both the redshift and the morphology. However, the contradictory results obtained by other researchers for and against including morphological data to increase the redshift estimate accuracy (Tagliaferri et al 2003;Singal et al 2011;Menou 2019;Wilson et al 2020) suggest that the morphological properties provide limited information to attain this goal. Therefore, we used bootstrap resampling to analyze the effect of the galaxy classification probabilities passed on the redshift module in our neural network model.…”

21st Century Statistical and Computational Challenges in Astrophysics

“…Galaxy morphology from images can supplement photometric measurements when neural networks are used. More recently, some more advanced methods have been used, including the use of different flavors of deep convolutional networks to derive photometric redshift directly from multi-band images (D'Isanto & Polsterer 2018, Pasquet et al 2019, and to build a morphology-aware photo-z estimator (Menou 2019). Comparisons of performance are presented by Dahlen et al (2013), Rau et al (2015), Salvato et al (2019), andSchmidt et al (2020).…”