Computer Science – Learning
Scientific paper
Dec 2005
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005aas...207.7311b&link_type=abstract
American Astronomical Society Meeting 207, #73.11; Bulletin of the American Astronomical Society, Vol. 37, p.1283
Computer Science
Learning
Scientific paper
The Supernova / Acceleration Probe (SNAP) is a planned satellite observatory that will investigate the dark energy by producing imaging data over a large (several square-degree) field of sky that will rival or exceed the Hubble Ultra Deep Field in photometric quality and depth. As such, SNAP is ideally suited for deep surveys as auxiliary science. We discuss application of quasar science techniques to SNAP photometry. Based on a simple photometric quasar / Lyman forest model, we simulate the population of quasars that SNAP will observe and compare the resulting photometry with a population of model stellar photometry. We examine the effectiveness of identifying quasars based only on photometric data by a variety of techniques, most of which were first developed for use with Sloan Digital Sky Survey. Exclusion of the stellar locus in the style of Newberg & Yanni, statistical mapping, and machine learning with neural networks are among the techniques we explore. A photometric redshift calculus is also presented.
Brondel Brian J.
Mufson Stuart Lee
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