The exploration of exoplanets has revolutionized our understanding of the universe, especially in the search for habitable planets beyond our solar system. This research investigates how well the Lightkurve Python tool helps scientists find new planets outside our solar system, utilizing time-series data from NASA's Kepler and TESS satellite telescopes. The focus is to identify Earth-sized planets, aiming to showcase the robust capabilities of Lightkurve in accurately identifying exoplanets and unveiling new candidates.
The Lightkurve module, with its intrinsic transit method, proves to be a powerful tool for monitoring stellar dimming during planetary transits. This research demonstrates its utility in navigating complex datasets and enhancing the detection sensitivity for smaller exoplanets. Lightkurve's transit method is a powerful and efficient way to uncover essential details about exoplanets, such as size, mass, and orbital characteristics. This approach significantly enhances our comprehension of the formation and evolution of exoplanetary systems, outperforming alternative techniques like gravitational microlensing and radial velocity..
The accompanying code employs the Lightkurve library to conduct a thorough analysis of photometric data from NASA's Kepler mission, focusing on the star KIC 6922244 - Kepler-8 in the constellation Lyra. The code determines the orbital period and transit time of a potential exoplanet, creating a phase-folded light curve for visualizing and confirming periodic patterns.
The method uses a tool called a periodogram to find repeating patterns in the data, helping to discover possible exoplanets. The code then organizes the light data based on these patterns, allowing a closer look at the characteristics of the planetary system. This method helps find exoplanets in the data and provides useful information for broader astronomical research. Overall, the study and accompanying code showcase the significance of the Lightkurve module in advancing our understanding of exoplanetary systems and their diverse characteristics.