Extending the SAGA Survey (xSAGA). I. Satellite Radial Profiles as a Function of Host-galaxy Properties

We present Extending the Satellites Around Galactic Analogs Survey (xSAGA), a method for identifying low-z galaxies on the basis of optical imaging and results on the spatial distributions of xSAGA satellites around host galaxies. Using spectroscopic redshift catalogs from the SAGA Survey as a training data set, we have optimized a convolutional neural network (CNN) to identify z < 0.03 galaxies from more-distant objects using image cutouts from the DESI Legacy Imaging Surveys. From the sample of >100,000 CNN-selected low-z galaxies, we identify >20,000 probable satellites located between 36-300 projected kpc from NASA-Sloan Atlas central galaxies in the stellar-mass range 9.5 < log(M-*/M-circle dot) < 11. We characterize the incompleteness and contamination for CNN-selected samples and apply corrections in order to estimate the true number of satellites as a function of projected radial distance from their hosts. Satellite richness depends strongly on host stellar mass, such that more-massive host galaxies have more satellites, and on host morphology, such that elliptical hosts have more satellites than disky hosts with comparable stellar masses. We also find a strong inverse correlation between satellite richness and the magnitude gap between a host and its brightest satellite. The normalized satellite radial distribution between 36-300 kpc does not depend on host stellar mass, morphology, or magnitude gap. The satellite abundances and radial distributions we measure are in reasonable agreement with predictions from hydrodynamic simulations. Our results deliver unprecedented statistical power for studying satellite galaxy populations and highlight the promise of using machine-learning for extending galaxy samples of wide-area surveys.

Automated summary

We present a method for identifying low-z galaxies based on optical imaging, and provide results on the spatial distributions of satellites around host galaxies. Using a convolutional neural network (CNN), we identify low-z galaxies and determine the true number and radial distribution of satellites. The results show that satellite richness depends on host stellar mass and morphology, while the radial distribution is independent of host characteristics. Our findings are in agreement with predictions from hydrodynamic simulations and offer statistical power for studying satellite galaxy populations.