1999arXiv (Cornell University)Open access

Photometric Redshifts in Hubble Deep Field South

D. L. Clements

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Abstract

We apply both a traditional `dropout' approach and a photometric redshift estimation technique to the Hubble Deep Field South data. We give a list of dropout selected z$\sim$3 objects, and show their images. We then discuss our photometric redshift estimation technique, demonstrate both its effectiveness and the role played by near-IR data, and then apply it to HDF-S to obtain an estimated redshift distribution.

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We apply both a traditional `dropout' approach and a photometric redshift estimation technique to the Hubble Deep Field South data. We give a list of dropout selected z$\sim$3 objects, and show their images. We then discuss our photometric redshift estimation technique, demonstrate both its effectiveness and the role played by near-IR data, and then apply it to HDF-S to obtain an estimated redshift distribution.

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Available abstract

We apply both a traditional `dropout' approach and a photometric redshift estimation technique to the Hubble Deep Field South data. We give a list of dropout selected z$\sim$3 objects, and show their images. We then discuss our photometric redshift estimation technique, demonstrate both its effectiveness and the role played by near-IR data, and then apply it to HDF-S to obtain an estimated redshift distribution.

Key concepts: Redshift, Photometric redshift, Hubble Deep Field, Hubble Ultra-Deep Field, Dropout (neural networks), Physics, Astrophysics, Field (mathematics)

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