The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
ANN z: Estimating Photometric Redshifts Using Artificial Neural Networks
Open accessAbstract
We introduce ANN z , a freely available software package for photometric redshift estimation using artificial neural networks. ANN z learns the relation between photometry and redshift from an appropriate training set of galaxies for which the redshift is already known. Where a large and representative training set is available, ANN z is a highly competitive tool when compared with traditional template‐fitting methods. The ANN z package is demonstrated on the Sloan Digital Sky Survey Data Release 1, and for this particular data set the rms redshift error in the range 0≲z≲0.7 is σ rms = 0.023. Nonideal conditions (spectroscopic sets that are small or brighter than the photometric set for which redshifts are required) are simulated, and the impact on the photometric redshift accuracy is assessed. 2
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A HIGHLY CONSISTENT FRAMEWORK FOR THE EVOLUTION OF THE STAR-FORMING “MAIN SEQUENCE” FROM z ∼ 0-6
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COSMOS PHOTOMETRIC REDSHIFTS WITH 30-BANDS FOR 2-deg2
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Rotation-invariant convolutional neural networks for galaxy morphology prediction
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The Fifth Data Release of the Sloan Digital Sky Survey
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Cosmology with cosmic shear observations: a review
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Metadata
- Authors
- Collister, A. A., & Lahav, O.
- Venue
- Publications of the Astronomical Society of the Pacific
- Publisher
- IOP Publishing
- Published
- 2004-04
- Volume
- 116
- Issue
- 818
- Pages
- 345-351
- ISSN
- 0004-6280 (ISSN-L)
- 1538-3873
- OpenAlex
- W2152876174
- Cited by
- 512
- References
- 19
- FWCI
- 8.47
- Type
- journal article
- Language
- en
- Open access
- bronze
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