Project:	Classification of Exoplanets (JPL AO No. 16153)
Disciplines:	Computer Science, Computer Science or Physics
Mentor:	Jonathan Jiang, (JPL), Jonathan.H.Jiang@jpl.nasa.gov, Phone: (818) 354-7135
Mentor URL:	https://science.jpl.nasa.gov/people/JJiang/  (opens in new window)
Background:	Since the detection of the very first exoplanet in 1992, humans have gained a new perspective of their place in the cosmos. That first detection ended up flourishing into the field of exoplanetary science and we find ourselves currently in a golden era of new discovery. We have found worlds rich in diversity which inspire us to think beyond the limits of our own Solar System. Our ultimate objective of this proposed work is to create a classification scheme for exoplanets that can characterize the diversity of planets in the context of understanding the formation and evolution processes of star-planetary systems. Our goal is to develop a universally accepted, data driven taxonomy that allows for accurate representation of any planet. Though most research in current academia focuses on very specific questions, our work is motivated by the overall architecture of exoplanetary systems and bigger picture ideas in order to gain a better and more accurate perspective of planets and stars within our galaxy and ultimately the universe. We hypothesize that the initial physical parameters of the nebula, in which stars and planets are formed, determine the properties of exoplanets and the systems that we observe today.
Description:	This project includes the following tasks: Task 1: Observational Data Analysis. Use data from NASA Exoplanet Archive to conduct detailed analysis of exoplanets and their host star properties. We will quantify physical relations among: (a) spectral type, metallicity, mass, angular momentum of the stars; (b) mass, angular momentum of the planetary system; (c) mass, radius, orbit, density and temperature of the planets. Task 2: Bias Analysis and Numerical Simulation. The relations among physical parameters found in Task 1 contain biases caused by the method of observation. Thus, we will use simulations to assess the biases and analyze different populations of samples. Task 3: Building a Robust Classification System.
Student Requirements:	Computer Science, Basic Physics and Astronomy
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    Caltech students only  Click on a program name for program info and application requirements.