The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-$z$ range better than other methods on the same data.
Astrophysics of Galaxies
1 code implementation • 6 Jul 2020 • S. Birrer, A. J. Shajib, A. Galan, M. Millon, T. Treu, A. Agnello, M. Auger, G. C. -F. Chen, L. Christensen, T. Collett, F. Courbin, C. D. Fassnacht, L. V. E. Koopmans, P. J. Marshall, J. -W. Park, C. E. Rusu, D. Sluse, C. Spiniello, S. H. Suyu, S. Wagner-Carena, K. C. Wong, M. Barnabè, A. S. Bolton, O. Czoske, X. Ding, J. A. Frieman, L. Van de Vyvere
Our calculation is based on a new hierarchical approach in which the MST is only constrained by stellar kinematics.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Secondly, we train a convolutional neural network (CNN) on Pan-STARRS gri image cutouts to classify this sample and obtain sets of 105760 and 12382 lens candidates with scores pCNN>0. 5 and >0. 9, respectively.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics