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We present "Extending the Satellites Around Galactic Analogs Survey" (xSAGA),a method for identifying low-$z$ galaxies on the basis of optical imaging, andresults on the spatial distributions of xSAGA satellites around host galaxies.Using spectroscopic redshift catalogs from the SAGA Survey as a training dataset, we have optimized a convolutional neural network (CNN) to identify $z <0.03$ galaxies from more distant objects using image cutouts from the DESILegacy Imaging Surveys. From the sample of $> 100,000$ CNN-selected low-$z$galaxies, we identify $>20,000$ probable satellites located between 36-300projected kpc from NASA-Sloan Atlas central galaxies in the stellar mass range$9.5 < \log(M_\star/M_\odot) < 11$. We characterize the incompleteness andcontamination for CNN-selected samples, and apply corrections in order toestimate the true number of satellites as a function of projected radialdistance from their hosts. Satellite richness depends strongly on host stellarmass, such that more massive host galaxies have more satellites, and on hostmorphology, such that elliptical hosts have more satellites than disky hostswith comparable stellar masses. We also find a strong inverse correlationbetween satellite richness and the magnitude gap between a host and itsbrightest satellite. The normalized satellite radial distribution between36-300 kpc does not depend strongly on host stellar mass, morphology, ormagnitude gap. The satellite abundances and radial distributions we measure arein reasonable agreement with predictions from hydrodynamic simulations. Ourresults deliver unprecedented statistical power for studying satellite galaxypopulations, and highlight the promise of using machine learning for extendinggalaxy samples of wide-area surveys.

Extending the SAGA Survey (xSAGA) I: Satellite Radial Profiles as a Function of Host Galaxy Properties