Machine Learning Implementation on TTV Analysis using TTVFast

Abstract Exoplanet research has undergone rapid development lately due to the increasing number of space telescopes and satellite surveys launched in recent years. Two missions with the most exoplanet discoveries using transit methods are Kepler and TESS missions. Both missions focused on photometric surveys to detect planets by observing the periodic dimming of a stars brightness as a planet passes in front of it. There is another method that could take advantage of transit observations called transit timing variation method. By observing the deviation of the transit time of transiting planet we could infer the existence of another body in the system. Performing exoplanet parameter estimation using TTV data is an extensive process. Estimating exoplanet parameters using TTV, we must fit the TTV data using a wide range of parameters using n-body simulation. N-body simulation in this scale is computationally costly. To estimate planet parameters, we need to run thousands of n-body simulations. With the increasing trend of using machine learning methods in the research process, we try to implement the machine learning method to make TTV analysis more effective and efficient. We implement the application of machine learning to n-body simulation using REBOUND and TTVFAST and compare the results. REBOUND is a Python-based library designed for simulating the dynamics of n-body systems, particularly celestial bodies like planets, stars, and other objects that interact gravitationally. While TTVFAST is a modified n-body simulation code that is specifically designed to calculate TTV on transiting planetary systems. We found that TTVFAST is much faster than REBOUND when generating samples for training and testing while still maintaining similar accuracy. Also, the machine learning model generated from both data samples is performed similarly.