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The CoRoT space mission was operating for almost 6 years, producing thousands of continuous photometric light curves. The temporal series of exposures are processed by the production pipeline, correcting the data for known instrumental effects. But even after these model-based corrections, some collective trends are still visible in the light curves. We propose here a simple exposure-based algorithm to remove instrumental effects. The effect of each exposure is a function of only two instrumental stellar parameters, position on the CCD and photometric aperture. The effect is not a function of the stellar flux, and therefore much more robust. As an example, we show that the $sim2%$ long-term variation of the early run LRc01 is nicely detrended on average. This systematics removal process is part of the CoRoT legacy data pipeline.
We present a matched-filter based algorithm for transit detection and its application to simulated COROT light curves. This algorithm stems from the work by Borde, Rouan & Leger (2003). We describe the different steps we intend to take to discriminat
Surveys for exoplanetary transits are usually limited not by photon noise but rather by the amount of red noise in their data. In particular, although the CoRoT spacebased survey data are being carefully scrutinized, significant new sources of system
Within the last years, the classification of variable stars with Machine Learning has become a mainstream area of research. Recently, visualization of time series is attracting more attention in data science as a tool to visually help scientists to r
Instrumental data are affected by systematic effects that dominate the errors and can be relevant when searching for small signals. This is the case of the K2 mission, a follow up of the Kepler mission, that, after a failure on two reaction wheels, h
We propose a new information theoretic metric for finding periodicities in stellar light curves. Light curves are astronomical time series of brightness over time, and are characterized as being noisy and unevenly sampled. The proposed metric combine