Finding high-redshift strong lenses in DES using convolutional neural networks

Jacobs, C.; Collett, T.; Glazebrook, K.; McCarthy, C.; Qin, A. K.; Abbott, T. M. C.; Abdalla, F. B.; Annis, J.; Avila, S.; Bechtol, K.; Bertin, E.; Brooks, D.; Buckley-Geer, E.; Burke, D. L.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; da Costa, L. N.; Davis, C.; De Vicente, J.; Desai, S.; Diehl, H. T.; Doel, P.; Eifler, T. F.; Flaugher, B.; Frieman, J.; Garcia-Bellido, J.; Gaztanaga, E.; Gerdes, D. W.; Goldstein, D. A.; Grün, D.; Gründl, A.; Gschwend, J.; Gutierrez, G.; Hartley, W. G.; Hollowood, D. L.; Honscheid, K.; Hoyle, B.; James, D. J.; Kühn, K.; Kuropatkin, N.; Lahav, O.; Li, T. S.; Lima, M.; Lin, H.; Maia, M. A. G.; Martini, P.; Miller, C. J.; Miquel, R.; Nord, B.; Plazas, A. A.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Serrano, S.; Sevilla-Noarbe, I.; Smith, M.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Vikram, V.; Walker, A. R.; Zhang, Y. und Zuntz, J. (2019): Finding high-redshift strong lenses in DES using convolutional neural networks. In: Monthly Notices of the Royal Astronomical Society, Bd. 484, Nr. 4: S. 5330-5349

Abstract

We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250 000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 < g - i < 5, 0.6 < g - r < 3, r_mag > 19, g_mag > 20, and i_mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7301 galaxies. During visual inspection, we rate 84 as 'probably' or 'definitely' lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations, we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Physik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 530 Physik
• ISSN: 0035-8711
• Sprache: Englisch