CoRoT Data Reduction By Example

TL;DR

This paper presents tailored data reduction techniques for CoRoT N2 lightcurves, enhancing signal quality and extracting additional information, with methods applicable to other space missions like Kepler and Plato.

Contribution

It introduces specific algorithms for CoRoT data processing, including noise reduction, gap filling, and signal detection, improving upon previous planet-focused methods.

Findings

Improved signal-to-noise ratio using a 1D drizzle algorithm

Effective gap filling with linear interpolation

Methods applicable to Kepler and Plato data

Abstract

Data reduction techniques published so far for the CoRoT N2 data product were targeted primarily on the detection of extrasolar planets. Since the whole dataset has been released, specific algorithms are required to process the lightcurves from CoRoT correctly. Though only unflagged datapoints must be chosen for scientific processing, some flags might be reconsidered. The reduction of data along with improving the signal-to-noise ratio can be achieved by applying a one dimensional drizzle algorithm. Gaps can be filled by linear interpolated data without harming the frequency spectrum. Magnitudes derived from the CoRoT color channels might be used to derive additional information about the targets. Depending on the needs, various filters in the frequency domain remove either the red noise background or high frequency noise. The autocorrelation function or the least squares periodogram are appropriate methods to identify periodic signals.The methods described here are not strictly limited to CoRoT data but may also be applied on Kepler data or the upcoming Plato mission.