Search

Precise physical properties of the known transiting exoplanets are essential for their precise atmospheric characterization using modern and upcoming instruments. Leveraging the large volume of high-signal-to-noise-ratio photometric follow-up data from TESS, highly precise physical properties can be estimated for these systems, especially for those discovered using ground-based instruments prior to the TESS mission. In this work, I have used the publicly available TESS follow-up data for 28 transiting systems with 10 < V _mag < 10.5, with an aim to update their known physical properties. The observed lightcurves have been analyzed by implementing a state-of-the-art critical noise treatment algorithm to effectively reduce both time-correlated and uncorrelated noise components, using sophisticated techniques like wavelet denoising and Gaussian-process regression. Compared with the previous studies, the estimated transit parameters are found to be more precise for most of the targets, including a few cases where a larger space-based instrument like Spitzer, Kepler, or CHEOPS has been used in the previous study. The large volume of transit observations used for each target has also resulted in a more accurate estimation of the physical properties, as this overcomes any error in parameter estimations from bias present in a smaller volume of data. Thus, comparing with the literature values, statistically significant improvements in the known physical properties of several targeted systems have been reported from this work. The large volume of transit-timing information from the analyses was also used to search for transit-timing variation trends in these targets, which has resulted in no significant detection.

The Gaussian process (GP) has gained much attention in cosmology due to its ability to reconstruct cosmological data in a model-independent manner. In this study, we compare two methods for GP kernel selection: approximate Bayesian computation (ABC) rejection and nested sampling. We analyze three types of data: cosmic chronometer data, type Ia supernovae data, and gamma-ray burst data, using five kernel functions. To evaluate the differences between kernel functions, we assess the strength of evidence using Bayes factors. Our results show that, for ABC rejection, the Matérn kernel with ν = 5/2 (M52 kernel) outperformes the commonly used radial basis function (RBF) kernel in approximating all three data sets. Bayes factors indicate that the M52 kernel typically supports the observed data better than the RBF kernel but with no clear advantage over other alternatives. However, nested sampling gives different results, with the M52 kernel losing its advantage. Nevertheless, Bayes factors indicate no significant dependence of the data on each kernel.

The Transiting Exoplanet Survey Satellite (TESS) follow-up of a large number of known transiting exoplanets provides a unique opportunity to study their physical properties more precisely. Being a space-based telescope, the TESS observations are devoid of any noise component resulting from the interference of Earth’s atmosphere. TESS also provides a greater probability to observe subsequent transit events owing to its longer uninterrupted time-series observations compared to ground-based telescopes. For the exoplanets around bright host stars in particular, TESS time-series observations provide high signal-to-noise ratio (S/N) lightcurves, which can be used for higher-precision studies for these exoplanets. In this work, I have studied the TESS transit photometric follow-up observations of 28 exoplanets around bright stars with V _mag ≤ 10. The already high-S/N lightcurves from TESS have been further processed with a critical noise-treatment algorithm, using the wavelet-denoising and the Gaussian-process regression techniques, to effectively reduce the noise components, both correlated and uncorrelated in time, which were then used to estimate the physical properties of these exoplanets. The study has resulted in very precise values for the physical properties of the target exoplanets, with the improvements in precision being significant for most of the cases compared to the previous studies. Also, since a comparatively large number of transit lightcurves from TESS observations were used to estimate these physical properties for each of the target exoplanets, which removes any bias due to the lack of sufficient data sets, these updated physical properties can be considered extremely accurate and reliable for future studies.