International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
www.irjet.net
Amelioration of the Lightkurve Package: Advancing Exoplanet Detection through the Transit Method Shreyas Tembhare1, Atharva Patil2, Soham Gadre3, Shraddha V. Pandit4 1Student, Dept. of Artificial Intelligence and Machine Learning, PES’s Modern College Of Engineering, Pune,
Maharashtra, India
2Student, Dept. of Artificial Intelligence and Machine Learning, PES’s Modern College Of Engineering, Pune,
Maharashtra, India
3Student, Dept. of BS-MS major in Physics ,Indian Institute of Science Education and Research, Pune,
Maharashtra, India
4Associate Professor, Dept. of Artificial Intelligence and Machine Learning, PES’s Modern College Of Engineering,
Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.INTRODUCTION Abstract - Identifying exoplanets has significantly impacted how astronomers perceive our galaxy and beyond, particularly in uncovering possible life sustaining planets outside our solar system. Astronomers' understanding of our galaxy and the universe beyond has greatly changed as a result of the discovery of exoplanets, especially those that may harbour life. Through the examination of time-series data from the Kepler and TESS satellite telescopes, our study investigates the efficacy of the Lightkurve Python module in the discovery of exoplanets. We demonstrate the module's capacity to precisely identify known exoplanets and find new candidates by concentrating on small Earth-sized planets. The transit approach employed by Lightkurve, which monitors the dimming of a star when a planet passes in front of it, is a user-friendly method for studying massive datasets and increasing the detection of small exoplanets. In comparison to other techniques like gravitational microlensing and radial velocity, the transit method using Lightkurve provides useful information on exoplanet properties like size, mass, and orbital characteristics, furthering our understanding of the formation and evolution of exoplanetary systems.. While there are certain limits, such as how susceptible one is to experimental artefacts and the detectability of specific exoplanet types, our findings emphasise Lightkurve's potential effect on exoplanet research. Technological improvements and the availability of new data from space observatories give prospects to improve the efficiency and accuracy of exoplanet finding and characterisation even further. The discovery and characterisation of exoplanets has transformed our understanding of the cosmos and widened our investigation into the possibility of life outside our solar system. Lightkurve provides fresh possibilities for discovering the nature and diversity of exoplanetary systems by simplifying and expediting the processing and analysis of time-series data.
The discovery and characterisation of exoplanets has altered our understanding of the universe and the prospect of life outside our solar system. Scientists have discovered thousands of exoplanets in numerous planetary systems thanks to the development of sophisticated telescopes and data analysis methods.[1]The transit method, one of several used to find exoplanets, has shown to be particularly successful at finding exoplanets by monitoring the periodic dimming of a host star's brightness brought on by a planet passing in front of it. In this study, we evaluate the effectiveness and potential of the Lightkurve Python package for exoplanet detection with an emphasis on the transit approach.[2] Specifically created for the analysis of time-series photometric data from space telescopes like Kepler, TESS, and upcoming missions like PLATO, Lightkurve is a user-friendly and flexible Python tool. It is a useful tool for exoplanet research because of its functionality, which includes periodogram analysis, light curve visualization, and transit modeling. The goal of this study is to evaluate Lightkurve's performance in finding exoplanets, especially small ones the size of Earth, and to compare its effectiveness to other widely used techniques like the radial velocity approach and gravitational microlensing.[3]. We hope to demonstrate the capability of Lightkurve in precisely identifying known exoplanets and maybe finding new exoplanet candidates by utilising the massive data made accessible from space telescopes. Understanding the efficiency and limitations of the Lightkurve module is crucial for maximizing the scientific output from the vast exoplanet datasets and ensuring the accuracy of exoplanet characterization. By examining the transit method using Lightkurve, we can determine the sensitivity to various exoplanet types, assess the impact of
Key Words: Exoplanets, Life sustaining planets,Timeseries data, TESS, Kepler, Transit approach.
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