Finding high-redshift strong lenses in DES using convolutional neural networks. (arXiv:1811.03786v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Jacobs_C/0/1/0/all/0/1">C. Jacobs</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Collett_T/0/1/0/all/0/1">T. Collett</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Glazebrook_K/0/1/0/all/0/1">K. Glazebrook</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+McCarthy_C/0/1/0/all/0/1">C. McCarthy</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Qin_A/0/1/0/all/0/1">A.K. Qin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Abbott_T/0/1/0/all/0/1">T. M. C. Abbott</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Abdalla_F/0/1/0/all/0/1">F. B. Abdalla</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Annis_J/0/1/0/all/0/1">J. Annis</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Avila_S/0/1/0/all/0/1">S. Avila</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bechtol_K/0/1/0/all/0/1">K. Bechtol</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bertin_E/0/1/0/all/0/1">E. Bertin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Brooks_D/0/1/0/all/0/1">D. Brooks</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Buckley_Geer_E/0/1/0/all/0/1">E. Buckley-Geer</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Burke_D/0/1/0/all/0/1">D. L. Burke</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Rosell_A/0/1/0/all/0/1">A. Carnero Rosell</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kind_M/0/1/0/all/0/1">M. Carrasco Kind</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Carretero_J/0/1/0/all/0/1">J. Carretero</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Costa_L/0/1/0/all/0/1">L. N. da Costa</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Davis_C/0/1/0/all/0/1">C. Davis</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Vicente_J/0/1/0/all/0/1">J. De Vicente</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Desai_S/0/1/0/all/0/1">S. Desai</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Diehl_H/0/1/0/all/0/1">H. T. Diehl</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Doel_P/0/1/0/all/0/1">P. Doel</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Eifler_T/0/1/0/all/0/1">T. F. Eifler</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Flaugher_B/0/1/0/all/0/1">B. Flaugher</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Frieman_J/0/1/0/all/0/1">J. Frieman</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bellido_J/0/1/0/all/0/1">J. Garc&#xed;a- Bellido</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gaztanaga_E/0/1/0/all/0/1">E. Gaztanaga</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gerdes_D/0/1/0/all/0/1">D. W. Gerdes</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Goldstein_D/0/1/0/all/0/1">D. A. Goldstein</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gruen_D/0/1/0/all/0/1">D. Gruen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gruendl_R/0/1/0/all/0/1">R. A. Gruendl</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gschwend_J/0/1/0/all/0/1">J. Gschwend</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gutierrez_G/0/1/0/all/0/1">G. Gutierrez</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hartley_W/0/1/0/all/0/1">W. G. Hartley</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hollowood_D/0/1/0/all/0/1">D. L. Hollowood</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Honscheid_K/0/1/0/all/0/1">K. Honscheid</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hoyle_B/0/1/0/all/0/1">B. Hoyle</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+James_D/0/1/0/all/0/1">D. J. James</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kuehn_K/0/1/0/all/0/1">K. Kuehn</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kuropatkin_N/0/1/0/all/0/1">N. Kuropatkin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lahav_O/0/1/0/all/0/1">O. Lahav</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Li_T/0/1/0/all/0/1">T. S. Li</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lima_M/0/1/0/all/0/1">M. Lima</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lin_H/0/1/0/all/0/1">H. Lin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Maia_M/0/1/0/all/0/1">M. A. G. Maia</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Martini_P/0/1/0/all/0/1">P. Martini</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Miller_C/0/1/0/all/0/1">C. J. Miller</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Miquel_R/0/1/0/all/0/1">R. Miquel</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Nord_B/0/1/0/all/0/1">B. Nord</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Plazas_A/0/1/0/all/0/1">A. A. Plazas</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sanchez_E/0/1/0/all/0/1">E. Sanchez</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Scarpine_V/0/1/0/all/0/1">V. Scarpine</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Schubnell_M/0/1/0/all/0/1">M. Schubnell</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Serrano_S/0/1/0/all/0/1">S. Serrano</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sevilla_Noarbe_I/0/1/0/all/0/1">I. Sevilla-Noarbe</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Smith_M/0/1/0/all/0/1">M. Smith</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Soares_Santos_M/0/1/0/all/0/1">M. Soares-Santos</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sobreira_F/0/1/0/all/0/1">F. Sobreira</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Suchyta_E/0/1/0/all/0/1">E. Suchyta</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Swanson_M/0/1/0/all/0/1">M. E. C. Swanson</a>, et al. (5 additional authors not shown)

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 7,301 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
9,428 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. Based on simulations we estimate our
sample to contain most discoverable lenses in this imaging and at this redshift
range.

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 7,301 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
9,428 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. Based on simulations we estimate our
sample to contain most discoverable lenses in this imaging and at this redshift
range.

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