Classification and redshift estimation by principal component analysis
1
Institut d'Astrophysique de Paris, CNRS / Univ. Pierre et Marie Curie, 98 bis Bd. Arago, 75014 Paris, France
2
European Southern Observatory, Vitacura, Alonso de Cordova, 3107, casilla 19001, Santiago, Chile
3
Dept. of Physics & Astronomy, Univ. British Columbia, 2219 Main Mall, Vancouver, BC V6T1Z4, Canada
Corresponding author: R. A. Cabanac, rcabanac@eso.org
Received:
3
April
2001
Accepted:
22
April
2002
We show that the first 10 eigencomponents of the
Karhunen-Loève expansion or Principal Component Analysis (PCA) provide
a robust classification scheme for the identification of stars, galaxies and
quasi-stellar objects from multi-band photometry. To quantify the efficiency
of the method, realistic simulations are performed which match the planned
Large Zenith Telescope survey. This survey is expected to provide spectral
energy distributions with a resolution 
for ~106 galaxies
to 
(
), 
QSOs, and 
stars.
We calculate that for a median signal-to-noise ratio of 6, 98% of stars,
100% of galaxies and 93% of QSOs are correctly classified. These values
increase to 100% of stars, 100% of galaxies and 100% of QSOs at a
median signal-to-noise ratio of 10. The 10-component PCA also allows
measurement of redshifts with an accuracy of 
for galaxies with
, and to 
for QSOs with 
, at a
median signal-to-noise ratio of 6. At a median signal-to-noise ratio 20,
for galaxies with 
and for QSOs with 
(note that for a median 
ratio of 20, the bluest/reddest objects will
have a signal-to-noise ratio of 
2 in their reddest/bluest filters).
This redshift accuracy is inherent to the resolution
provided by the set of medium-band filters used by the Large Zenith
Telescope survey. It provides an accuracy improvement of nearly an order of
magnitude over the photometric redshifts obtained from broad-band BVRI
photometry.
Key words: galaxies: fundamental parameters / methods: data analysis / Surveys / galaxies: general
© ESO, 2002