NASA Hubble Fellowship Program
Sagan Postdoctoral Fellow
Rm. 605B, Campbell Hall
University of California at Berkeley
I am a NASA Hubble Fellowship Program (NHFP) Sagan Postdoctoral Fellow at UC Berkeley. I received my Ph.D. from Harvard University, where I worked with Sean Andrews and the CfA circumstellar disk group. My previous postdoc was at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC) at Stanford University, where I worked with the Gemini Planet Imager (GPI) group, led by Bruce Macintosh. I study a variety of topics related to young stars, protoplanetary disks, pre-main sequence evolution, and exoplanets.
Mass is the fundamental property that determines the evolutionary path of a star. In particular, the masses of young stars are of great relevance to many astrophysical problems, including star and planet formation. We have developed a novel approach that combines spatially resolved sub-millimeter spectral line imaging and optical/near-infrared high resolution spectroscopy to derive the fundamental properties of a young star: mass, temperature, and radius. By applying our technique to a sample of pre-main sequence stars, we are mapping out a dynamically-calibrated Hertzsprung-Russell diagram for the express purpose of evaluating pre-main sequence evolutionary models. I use the Atacama Large Millimeter Array (ALMA) to gain a multi-wavelength understanding of young stellar systems and understand the planet formation process.
View these posts for more detail about my projects
We have developed a framework for spectroscopic inference that we believe is a significant step towards fulfilling the astronomer’s dream of using all of the data, called Starfish. Our statistical framework attempts to overcome many of the difficulties inherent to deriving fundamental stellar properties from high resolution spectra. Principally, at high resolution and high sensitivity, model systematics—such as inaccuracies in the strengths of particular lines—will dominate the noise budget. We address these problems by accounting for the covariant structure of the residuals that can result from fitting models to data in this high signal-to-noise, high spectral resolution regime. Using some of the machinery developed by the field of Gaussian processes, we can parameterize the covariant structure both due to general line mis-matches as well as specific “outlier” spectral lines due to pathological errors in the atomic and molecular line databases.