Project:	Inferring intrinsic properties of observed exoplanetary systems (JPL AO No. 15279)
Disciplines:	Astronomy/Astrophysics, Computer Science
Mentor:	Yasuhiro Hasegawa, (JPL), Yasuhiro.Hasegawa@jpl.nasa.gov, Phone: (818) 393-7253
Background:	The rapid increase of observed exoplanets revolutionizes our understanding of planet formation. Despite such successes, intrinsic properties (i.e., the true multiplicity and the presence/absence of habitable planets) of observed exoplanetary systems remain elusive. This arises from observational biases; even if some planets are discovered in a system, all of the planets constituting the system cannot be observed. This issue stands out, especially for distant, small-sized planets including Earth-like, habitable planets. This project attempts to resolve such an issue, by combining the results of planet formation simulations and machine learning.
Description:	The objective of the project is to infer intrinsic properties of exoplanetary systems observed by Kepler space telescope. The project will be divided into three tasks. Task 1 is to generate training data sets, using the results of planet formation simulations. Thousands of artificial planetary systems will be produced, using distribution functions of planetary systems that are derived from numerical simulations. In order to take into account observational biases, an exoplanet observation simulator will be applied to the artificial planetary systems. A combination of the raw and biased planetary systems constitutes reliable training data sets because the former serves as the "ground truth". Task 2 is to train various classifiers, using the training data sets. These include random forest, support vector machine (SVM), and neural networks (NNs). Since characterizing multi-planet systems is challenging itself, this project will utilize the so-called statistical measures that are physical quantities devised to facilitate characterization of multi-planet systems. Adopting various classifiers will enable evaluation of which one would work best. Task 3 is to apply the trained classifiers to exoplanetary systems observed by Kepler. The outcome will be intrinsic properties of these systems such as the true multiplicity and the presence/absence of habitable planets. The participant will work on the above tasks under the supervision of Drs. Hasegawa, Hu, and other exoplanet scientists in Astrophysics and Space Sciences section at JPL.
Student Requirements:	Taking courses of physics, introductory astronomy, and computational sciences at the undergrad level or higher is strongly preferred, although not required. Previous research experience and strong motivation are highly taken into account.
Location / Safety:	Project building and/or room locations: . Student will need special safety training: .
Programs:	This AO can be done under the following programs:   Program    Available To        SURF@JPL    both Caltech and non-Caltech students  Click on a program name for program info and application requirements.