| Abstract : | Several space-based telescopes have been widely used to detect and characterize Exoplanets using the transit method. The Transiting Exoplanet Survey Satellite (TESS) is one such instrument, which is the part of a survey to detect new Exoplanets around nearby bright stars. The most important advantage that the space-based telescopes provide over their ground-based counterparts is that the observed data is free from any noise component due to the interference of Earth’s atmosphere. However, the noise components due to various instrumental effects and the stellar activity and pulsations still affect the photometric precision of the observed data, which limits the effectiveness of these facilities.
To tackle this, we have developed a critical noise treatment algorithm, using cutting-edge noise reduction techniques, such as the Wavelet denoising and the Gaussian process regression, to treat the photometric lightcurves and reduce the noise components both correlated and uncorrelated in time, originating from different sources. We have demonstrated the effectiveness of this algorithm by applying it to TESS transit photometric observations for five Exoplanets, namely KELT-7 b, HAT-P-14 b, WASP-29 b, WASP-95 b and WASP-156 b. By comparing the parameter values estimated by using our critical noise treatment algorithm to those without using it, we have shown how both the accuracy and precision of the estimated parameters have significantly improved. We have also compared our estimated results to the best-known parameter values of these targets from the previous studies, which shows a few orders of magnitudes improvements for every parameters, demonstrating the capability of these space-based observations when combined with the critical noise treatment techniques. The algorithm developed in this work can further be extended to other Exoplanets and will play a significant role in the study of Exoplanet atmospheres using next-generation telescopes, such as James Webb Space Telescope (JWST). |
