A&A 401, 743-753 (2003)
DOI: 10.1051/0004-6361:20030093
Detecting planetary transits in the presence of stellar variability
Optimal filtering and the use of colour information
S. Carpano1, S. Aigrain2 and F. Favata11 Astrophysics Division - Space Science Department of ESA, ESTEC, Postbus 299, 2200 AG Noordwijk, The Netherlands
e-mail: carpano@astro.uni-tuebingen;Fabio.Favata@rssd.esa.int
2 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
e-mail: suz@ast.cam.ac.uk
(Received 30 October 2002 / Accepted 9 January 2003)
Abstract
Space-based photometric transit searches, such as the ESA
mission Eddington (planned for launch in 2007), are expected
to detect large numbers of terrestrial planets outside the solar
system, including some Earth-like planets. Combining simplicity of
concept with efficiency, the transit method consists in detecting
the periodic luminosity drop in stellar light curves caused by the
transit of a planet in front of its parent star. In a previous paper
(Aigrain & Favata 2002, hereafter Paper I), we developed a Bayesian transit
detection algorithm and evaluated its performance on simulated light
curves dominated by photon noise. In this paper, we examine the
impact of intrinsic stellar variability. Running the algorithm on
light curves with added stellar variability (constructed using data
from the VIRGO/PMO6 instrument on board SoHO) demonstrated the need
for pre-processing to remove the stellar noise. We have developed an
effective variability filter, based on an ad-hoc optimal approach,
and run extensive simulations to test the filter and detection
algorithm combination for a range of stellar magnitudes and activity
levels. These show that activity levels up to solar maximum are not
an obstacle to habitable planet detection. We also evaluated the
benefits of using colour information in the detection process, and
concluded that in the case of Eddington they are outweighed
by the implied loss of photometric accuracy.
Key words: planetary systems -- occultations -- stars: microvariability -- methods: data analysis
Offprint request: F. Favata, Fabio.Favata@rssd.esa.int
© ESO 2003