QSO Selection and Photometric Redshifts with Neural Networks

Ch. Yeche; P. Petitjean; J. Rich; E. Aubourg; N. Busca; J. -Ch. Hamilton; J. -M. Le Goff; I. Paris; S. Peirani; Ch. Pichon; E. Rollinde; M. Vargas-Magana

arXiv · 0910.3770

QSO Selection and Photometric Redshifts with Neural Networks

Ch. Yeche·P. Petitjean·J. Rich·E. Aubourg·N. Busca·J. -Ch. Hamilton·J. -M. Le Goff·I. Paris·S. Peirani·Ch. Pichon·E. Rollinde·M. Vargas-Magana

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Abstract

Baryonic Acoustic Oscillations (BAO) and their effects on the matter power spectrum can be studied by using the Lyman-alpha absorption signature of the matter density field along quasar (QSO) lines of sight. A measurement sufficiently accurate to provide useful cosmological constraints requires the observation of ~100000 quasars in the redshift range 2.2<z<3.5 over ~8000 deg2. Such a survey is planned by the Baryon Oscillation Spectroscopic Survey (BOSS) project of the Sloan Digital Sky Survey (SDSS-III).In practice, one needs a stellar rejection of more than two orders of magnitude with a selection efficiency for quasars better than 50% up to magnitudes as large as g ~ 22. To obtain an appropriate target list and estimate quasar redshifts, we have developed an Artificial Neural Networks (NN) with a multilayer perceptron architecture. The input variables are photometric measurements, i.e. the object magnitudes and their errors in the five bands (ugriz) of the SDSS photometry. For target selection, we achieve a non-quasar point-like object rejection of 99.6% and 98.5% for a quasar efficiency of, respectively, 50% and 85%. The photometric redshift precision is of the order of 0.1 over the region relevant for BAO studies.

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Ch. Yeche, P. Petitjean, J. Rich, E. Aubourg, N. Busca, J. -Ch. Hamilton, J. -M. Le Goff, I. Paris, S. Peirani, Ch. Pichon, E. Rollinde, M. Vargas-Magana. 2009-10-20. QSO Selection and Photometric Redshifts with Neural Networks. https://arxiv.org/abs/0910.3770

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