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