Ian Czekala

Ian Czekala

KIPAC Postdoctoral Fellow
Rm. 216, Physics and Astronomy Building
Stanford University

I am a KIPAC Postdoctoral Fellow at Stanford University, where I work with Bruce Macintosh and the Gemini Planet Imager (GPI) group. I received my Ph.D. from Harvard University, where I worked with Sean Andrews and the CfA circumstellar disk group. 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. In the future, I am excited to use the Gemini Planet Imager (GPI) and 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

  • SAMSI Exoplanet Exploration Workshop In October 2016, I joined many statistically-oriented exoplaneteers at the Hierarchical Bayesian Modeling of Exoplanet Populations workshop at the Statistical and Applied Mathematics Institute (SAMSI) in North Carolina.
  • Starfish: Flexible Tools for Spectroscopic Inference

    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.