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Estimating photometric redshifts with artificial neural networks
Open accessAbstract
A new approach to estimating photometric redshifts — using artificial neural networks (ANNs) — is investigated. Unlike the standard template-fitting photometric redshift technique, a large spectroscopically-identified training set is required but, where one is available, ANNs produce photometric redshift accuracies at least as good as and often better than the template-fitting method. The Bayesian priors on the underlying redshift distribution are automatically taken into account. Furthermore, inputs other than galaxy colours — such as morphology, angular size and surface brightness — may be easily incorporated, and their utility assessed. Different ANN architectures are tested on a semi-analytic model galaxy catalogue and the results are compared with the template-fitting method. Finally, the method is tested on a sample of ∼20 000 galaxies from the Sloan Digital Sky Survey. The rms redshift error in the range z≲ 0.35 is σz∼ 0.021.
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Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
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Dieleman, S., Willett, K. W., & Dambre, J. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv632 · Cited by 781
ANN z: Estimating Photometric Redshifts Using Artificial Neural Networks
Collister, A. A., & Lahav, O. (2004) · Publications of the Astronomical Society of the Pacific · doi:10.1086/383254 · Cited by 512
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Data mining and machine learning in astronomy
BALL, N. M., & BRUNNER, R. J. (2010) · International Journal of Modern Physics D · doi:10.1142/s0218271810017160 · Cited by 383
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Walcher, J., Groves, B., Budavári, T., & Dale, D. (2011) · Astrophysics and Space Science · doi:10.1007/s10509-010-0458-z · Cited by 344
Metadata
- Authors
- Firth, A. E., Lahav, O., & Somerville, R. S.
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-03
- Volume
- 339
- Issue
- 4
- Pages
- 1195-1202
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2161966883
- Cited by
- 200
- References
- 1
- FWCI
- 3.48
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Firth, A. E., Lahav, O., & Somerville, R. S. (2003). Estimating photometric redshifts with artificial neural networks. Monthly Notices of the Royal Astronomical Society. https://doi.org/10.1046/j.1365-8711.2003.06271.x
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