Kavli Affiliate: Risa H. Wechsler
| First 5 Authors: John F. Wu, J. E. G. Peek, Erik J. Tollerud, Yao-Yuan Mao, Ethan O. Nadler
| Summary:
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
$9.5 < log(M_star/M_odot) < 11$. 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 strongly 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.
| Search Query: ArXiv Query: search_query=au:”Risa H. Wechsler”&id_list=&start=0&max_results=10