Multi-band transit follow up observations of five hot-Jupiters with critical noise treatments: Improved physical properties

TL;DR

This study presents multi-band transit observations of five hot-Jupiters using ground-based telescopes, employing advanced noise reduction techniques like Wavelet Denoising and Gaussian Process regression to improve the accuracy of derived planetary properties.

Contribution

The paper introduces a comprehensive noise treatment methodology combining Wavelet Denoising and Gaussian Process regression for ground-based transit photometry, enhancing the precision of exoplanet parameter estimation.

Findings

Improved physical property estimates of five hot-Jupiters.

Effective noise reduction in ground-based transit data.

Enhanced accuracy over previous studies.

Abstract

The most challenging limitation in transit photometry arises from the noises in the photometric signal. In particular, the ground-based telescopes are heavily affected by the noise due to perturbation in the Earth's atmosphere. Use of telescopes with large apertures can improve the photometric signal-to-noise ratio (S/N) to a great extent. However, detecting a transit signal out of a noisy light curve of the host star and precisely estimating the transit parameters call for various noise reduction techniques. Here, we present multi-band transit photometric follow-up observations of five hot-Jupiters e.g., HAT-P-30 b, HAT-P-54 b, WASP-43 b, TrES-3 b and XO-2 N b, using the 2m Himalayan Chandra Telescope (HCT) at the Indian Astronomical Observatory, Hanle and the 1.3m J. C. Bhattacharya Telescope (JCBT) at the Vainu Bappu Observatory, Kavalur. Our critical noise treatment approach includes techniques such as Wavelet Denoising and Gaussian Process regression, which effectively reduce both time-correlated and time-uncorrelated noise components from our transit light curves. In addition to these techniques, use of our state-of-the-art model algorithms have allowed us to estimate the physical properties of the target exoplanets with a better accuracy and precision compared to the previous studies.