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Quasar-host galaxy detection using the cross-correlation technique

Boyce, P. J., Phillipps, S. and Davies, Jonathan Ivor 1993. Quasar-host galaxy detection using the cross-correlation technique. Astronomy and Astrophysics 280 (2) , pp. 694-703.

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We show, using simulated data, that the two-component cross-correlation technique is a more discriminating method of determining the morphology of the host galaxy of a quasar than the Chi squared fitting of model intensity distributions to the quasar's radial intensity distribution which previous authors have used. We also show that even if the signal-to-noise is not high enough to enable galaxy morphology to be determined, a very accurate measure of the contribution of the central point source to the image can still be made. This enables much better measurements of mqso and mgal than is possible using the methods used by previous authors. We apply this method to an i-band image of PKS 2349-014 and show that the use of those used by previous authors would have led to significant errors in the measurement of mgal and mqso for this object compared to those values obtained using the cross correlation technique. Such errors could seriously affect the statistical relationships which have been derived between mqso and mgal for quasars and the relationships between the magnitudes of the host galaxies of radio-loud quasars and radio-quiet quasars and similar relationships between quasar host galaxies and radio galaxies. We suggest that a re-analysis of the data using the cross correlation method may affect the conclusions reached and should be undertaken. We also suggest that applying the technique to high S/N data may enable a determination of host galaxy morphology at a high confidence level.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Subjects: Q Science > QB Astronomy
Uncontrolled Keywords: cross correlation ; data reduction ; galactic structure ; galaxies ; image processing ; quasars ; astronomical models ; least squares method ; maximum likelihood estimates ; morphology ; point spread functions
Publisher: Springer
ISSN: 0004-6361
Last Modified: 04 Jun 2017 04:25

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