About Me

I'm a graduate student at the Institute for Astronomy at the University of Hawaii working with Daniel Huber and Eric Gaidos on finding and understanding new planets orbiting giant stars. Previously, I worked with Stuart Jefferies on understanding the subsurface structure of the Sun and Andrew Howard on a new method to describe stellar variability when measuring the masses of exoplanets..

I'm interested in understanding the mysteries behind stellar and planetary formation and evolution, and how they affect our past and future. In my free time, I enjoy making music, attempting to surf, and learning and teaching scientific programming skills in Python.

  • Curriculum Vitae

  • Publications

  • GitHub

  • Twitter



Planet Re-Inflation

A large fraction of gas giant planets with temperatures above 1000 K are much larger than 1.2 times the size of Jupiter, the maximum size models predict for a self-gravitating sphere of hydrogen and helium. Though these inflated giant planets have been known for over twenty years, the mechanism responsible for their inflation remains unclear. However, if we were to observe an inflated planet receiving a moderate amount of radiation from a red giant host star, such that the planet would have been too cool to inflate until the host star became a red giant, this would provide evidence for a planet inflation mechanism where the stellar irradiation deposited into the planet's interior causes it to expand. In December 2016, I discovered K2-97b, an inflated planet orbiting a red giant star every 8.4 days, and discovered its cosmic twin a year later. These planets' incident flux history indicate they were too cool to inflate until their host stars became red giants, and thus provide the first evidence that planets may be inflated directly by incident stellar radiation rather than by delayed loss of heat from formation. Assuming that gas planets begin their lives inflated due to heat from formation, K2-97b is likely the first known planet to be currently re-inflated. Future observations of these systems with NASA's Spitzer Space Telescope in 2018 will help clarify the inflation status of these planets.

New Methods For Analyzing Stellar Variability

In 2015, I published a paper with Andrew Howard and Raphaelle Haywood which used radial velocity observations of nearby stars to detect a Earth-sized planet. In order to measure a planet's mass from the radial velocity data from a star, it is often necessary to first characterize the background radial velocity noise due to the star or other non-astrophysical factors. In order to better characterize this predominant stellar activity signal, we can test different statistical models to remove the stellar signal and extract the planetary signal with greater precision. We used a Gaussian Process regression, a nonparametric statistical time-series analysis technique, and tuned parameters describing general characteristics of the data, to describe and remove the predominant stellar signal. Using this technique, we were able to achieve provide a more robust technique for detecting planets via radial velocity measurements. I'm currently working on extending this technique to account for stellar oscillation and granulation.

Planet Occurrence Around Evolved Stars

In order to detemine the lifetime of our Solar System, it is imperative to detemine the survival rate of planets around evolved stars. So far, our preliminary results indicate that hot Jupiters orbit around 1% of red giant stars, equivalent to the population of hot Jupiters around main sequence stars. Can most large planets survive red giant evolution? Stay tuned for more results!


Teaching Material

HISTAR Lightcurve Analysis Tutorial

Astro 300L Fourier Transform Demo

Scientific Coding with Python: Tutorials

Performing a Least Squares Fit to Data: Part 1 -- How to fit artificial data with a known shape using the least squares packages provided in Python 2. Comments welcome! Annotation functionality made possible thanks to Genius.

Making a Power Spectrum in Python -- A quick method to create a power spectrum of NASA HMI images using the n-dimensional Fast Fourier Transform function in Python 2.