Identification and redshift determination of quasi-stellar objects with medium-band photometry: application to Gaia

Details Identification and redshift determination of quasi-stellar objects with medium-band photometry: application to Gaia Identification and redshift determination of quasi-stellar objects with medium-band photometry: application to Gaia

Apr 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006mnras.367..879c&link_type=abstract

Monthly Notices of the Royal Astronomical Society, Volume 367, Issue 3, pp. 879-904.

Astronomy and Astrophysics

Astronomy

5

Methods: Data Analysis, Quasars: General

Scientific paper

All-sky, multicolour, medium deep (V~= 20) surveys have the potentiality of detecting several hundred thousands of quasi-stellar objects (QSOs). Spectroscopic confirmation is not possible for such a large number of objects, so that secure photometric identification and precise photometric determination of redshifts (and other spectral features) become mandatory. This is especially the case for the Gaia mission, in which QSOs play the crucial role of fixing the celestial referential frame, and in which more than 900 gravitationally lensed QSOs should be identified.
We first built two independent libraries of synthetic QSO spectra reflecting the most important variations in the spectra of these objects. These libraries are publicly available for simulations with any instrument and photometric system.
Traditional template fitting and artificial neural networks (ANNs) are compared to identify QSOs among the population of stars using broad- and medium-band photometry (BBP and MBP, respectively). Besides those two methods, a new one, based on the spectral principal components (SPCs), is also introduced to estimate the photometric redshifts. Generic trends as well as results specifically related to Gaia observations are given.
We found that (i) ANNs can provide clean, uncontaminated QSO samples suitable for the determination of the reference frame, but with a level of completeness decreasing from ~=50 per cent at the Galactic pole at V= 18 to ~= 16 per cent at V= 20; (ii) the χ2 approach identifies about 90 per cent (60 per cent) of the observed QSOs at V= 18 (V= 20), at the expense of a higher stellar contamination rate, reaching ~=95 per cent in the galactic plane at V= 20. Extinction is a source of confusion and makes difficult the identification of QSOs in the galactic plane and (iii) the χ2 method is better than ANNs to estimate the photometric redshifts. Due to colour degeneracies, the largest median absolute error (|Δz|Median~= 0.2) is predicted in the range 0.5 < zspec < 2. The method based on the SPCs is promisingly good at recovering the redshift, in particular for V < 19 and z < 2.5 QSOs. For bright (V<~ 18) QSOs, SPCs are also able to recover the spectral shape from the BBP and MBP data.

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