publication . Article . Preprint . Other literature type . 2019

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

Mathew Smith; J. Carretero; Paul Martini; Marcelle Soares-Santos; Joe Zuntz; D. L. Hollowood; Huan Lin; Keith Bechtol; Chris McCarthy; J. Annis; ...
Open Access English
  • Published: 21 Apr 2019
  • Publisher: HAL CCSD
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 cat...
Subjects
arXiv: Astrophysics::Cosmology and Extragalactic Astrophysics
free text keywords: gravitational lensing: strong, methods: statistical, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], strong [gravitational lensing], statistical [methods], Astronomy & Astrophysics, Astronomical and Space Sciences, Lentes gravitacionais, Estatística - Métodos, Gravitational lenses, Statistical methods, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, astro-ph.GA, astro-ph.IM, RCUK, STFC, AST-1138766, AST-1536171, Space and Planetary Science, Astronomy and Astrophysics, Imaging data, Methods statistical, Physics, Convolutional neural network, Lens (optics), law.invention, law, Combinatorics, Redshift
Funded by
EC| TESTDE
Project
TESTDE
Testing the Dark Energy Paradigm and Measuring Neutrino Mass with the Dark Energy Survey
  • Funder: European Commission (EC)
  • Project Code: 291329
  • Funding stream: FP7 | SP2 | ERC
,
NSF| Collaborative Research: The Dark Energy Survey Data Management Operations
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1138766
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
,
ARC| ARC Centres of Excellences - Grant ID: CE170100013
Project
  • Funder: Australian Research Council (ARC) (ARC)
  • Project Code: CE170100013
  • Funding stream: ARC Centres of Excellences
,
EC| COSMICDAWN
Project
COSMICDAWN
Understanding the Origin of Cosmic Structure
  • Funder: European Commission (EC)
  • Project Code: 306478
  • Funding stream: FP7 | SP2 | ERC
,
EC| COGS
Project
COGS
Capitalizing on Gravitational Shear
  • Funder: European Commission (EC)
  • Project Code: 240672
  • Funding stream: FP7 | SP2 | ERC
80 references, page 1 of 6

Abbott T. M. C., et al., 2018, arXiv:1801.03181 [astro-ph]

Agnello A., Kelly B. C., Treu T., Marshall P. J., 2015, MNRAS, 448, 1446

Alard C., 2006, arXiv:astro-ph/0606757

Amiaux J., et al., 2012, arXiv:1209.2228 [astro-ph 10.1117/12.926513, p. 84420Z

Avestruz C., Li N., Lightman M., Collett T. E., Luo W., 2017, preprint, 1704, arXiv:1704.02322

Barnabè M., Czoske O., Koopmans L. V. E., Treu T., Bolton A. S., 2011, MNRAS, 415

Bellstedt S., et al., 2018, MNRAS

Bolton A. S., Burles S., Koopmans L. V. E., Treu T., Moustakas L. A., 2006, ApJ, 638, 703

Bonvin V., et al., 2016, MNRAS, p. stw3006

Cao Z., Qin T., Liu T.-Y., Tsai M.-F., Li H., 2007, in Proceedings of the 24th International Conference on Machine Learning. ICML '07. ACM, New York, NY, USA, pp 129-136, doi:10.1145/1273496.1273513

Cappellari M., et al., 2011, MNRAS, 413, 813

Chan J. H. H., Suyu S. H., Chiueh T., More A., Marshall P. J., Coupon J., Oguri M., Price P., 2015, ApJ, 807

Choi Y.-Y., Park C., Vogeley M. S., 2007, ApJ, 658, 884

Chollet 2015, Keras

Collett T. E., 2015, ApJ, 811, 20

80 references, page 1 of 6
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 cat...
Subjects
arXiv: Astrophysics::Cosmology and Extragalactic Astrophysics
free text keywords: gravitational lensing: strong, methods: statistical, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], strong [gravitational lensing], statistical [methods], Astronomy & Astrophysics, Astronomical and Space Sciences, Lentes gravitacionais, Estatística - Métodos, Gravitational lenses, Statistical methods, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, astro-ph.GA, astro-ph.IM, RCUK, STFC, AST-1138766, AST-1536171, Space and Planetary Science, Astronomy and Astrophysics, Imaging data, Methods statistical, Physics, Convolutional neural network, Lens (optics), law.invention, law, Combinatorics, Redshift
Funded by
EC| TESTDE
Project
TESTDE
Testing the Dark Energy Paradigm and Measuring Neutrino Mass with the Dark Energy Survey
  • Funder: European Commission (EC)
  • Project Code: 291329
  • Funding stream: FP7 | SP2 | ERC
,
NSF| Collaborative Research: The Dark Energy Survey Data Management Operations
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1138766
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
,
ARC| ARC Centres of Excellences - Grant ID: CE170100013
Project
  • Funder: Australian Research Council (ARC) (ARC)
  • Project Code: CE170100013
  • Funding stream: ARC Centres of Excellences
,
EC| COSMICDAWN
Project
COSMICDAWN
Understanding the Origin of Cosmic Structure
  • Funder: European Commission (EC)
  • Project Code: 306478
  • Funding stream: FP7 | SP2 | ERC
,
EC| COGS
Project
COGS
Capitalizing on Gravitational Shear
  • Funder: European Commission (EC)
  • Project Code: 240672
  • Funding stream: FP7 | SP2 | ERC
80 references, page 1 of 6

Abbott T. M. C., et al., 2018, arXiv:1801.03181 [astro-ph]

Agnello A., Kelly B. C., Treu T., Marshall P. J., 2015, MNRAS, 448, 1446

Alard C., 2006, arXiv:astro-ph/0606757

Amiaux J., et al., 2012, arXiv:1209.2228 [astro-ph 10.1117/12.926513, p. 84420Z

Avestruz C., Li N., Lightman M., Collett T. E., Luo W., 2017, preprint, 1704, arXiv:1704.02322

Barnabè M., Czoske O., Koopmans L. V. E., Treu T., Bolton A. S., 2011, MNRAS, 415

Bellstedt S., et al., 2018, MNRAS

Bolton A. S., Burles S., Koopmans L. V. E., Treu T., Moustakas L. A., 2006, ApJ, 638, 703

Bonvin V., et al., 2016, MNRAS, p. stw3006

Cao Z., Qin T., Liu T.-Y., Tsai M.-F., Li H., 2007, in Proceedings of the 24th International Conference on Machine Learning. ICML '07. ACM, New York, NY, USA, pp 129-136, doi:10.1145/1273496.1273513

Cappellari M., et al., 2011, MNRAS, 413, 813

Chan J. H. H., Suyu S. H., Chiueh T., More A., Marshall P. J., Coupon J., Oguri M., Price P., 2015, ApJ, 807

Choi Y.-Y., Park C., Vogeley M. S., 2007, ApJ, 658, 884

Chollet 2015, Keras

Collett T. E., 2015, ApJ, 811, 20

80 references, page 1 of 6
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