High-Contrast Imaging of Exoplanets and Brown Dwarfs
Gas-giant planets of a few Jupiter-masses on orbits of 5–50 astronomical units (AU) around young stars in the solar vicinity are now directly observable with modern high-contrast imaging techniques, probing a new region of planet mass vs separation parameter space. My main research at the moment concerns direct imaging and spectroscopy of exoplanets with Project 1640 (P1640) at Palomar Observatory's 5-m Hale telescope. The immediate aim of P1640 is to obtain images and low-resolution spectra of young (<1 Gyr) gas-giant planets (and brown dwarfs) around A- and F type stars within 75 parsec (pc) of the Sun, in order to characterize planetary systems around nearby stars. A methodical survey of system characteristics derived from direct imaging and spectroscopy of exoplanets is essential to obtain more complete statistics on system frequencies and properties, and a better understanding of planet formation and evolution. This is also a key step to future imaging of Earth-like exoplanets.
Our 3-year survey was recently completed, and although some important new results on companions in benchmark systems like, e.g., HR 8799 (Oppenheimer et al. 2013, Pueyo et al. 2015), κ And (Hinkley et al. 2013), HD 19467 (Crepp et al. 2015), and GJ 758 (Nilsson et al. 2017) were obtained, confirmed discoveries have been few. This is in line with early results from GPI and SPHERE, leading to our preliminary conclusion that gas-giant planets at probed masses and orbital distances are less numerous than anticipated.
During this project I've gained significant experience in performing coronagraphic high-contrast hyperspectral imaging observations, and learned valuable lessons about the intricacies of storing and processing large amounts of survey data. Approximately 100 nights have been spent observing with P1640, with additional daytime setup and calibration of the coronagraphic system. As P1640 has a relatively small core team, I have been fortunate to touch on all aspects of the project; from strategic target selection, detailed planning of observations, instrument setup, and calibration, to tuning of the PALM-3000 (P3k; Dekany et al. 2013) AO and CAL (e.g., Vasisht et al. 2014, Zhai et al. 2012) systems, and subsequent data acquisition. I have made extensive use of PCXP (Zimmerman et al. 2011) for extraction of tightly packed microspectra from images into data cubes, as well as the PCA-based algorithms S4 (Fergus et al. 2014) and KLIP (Soummer et al. 2012) for speckle subtraction. Another important step in the data processing pipeline is correction of atmospheric and instrument induced dispersion, for which I wrote the cube alignment code (CACS; Nilsson et al., in prep.) implementing automatic astrometric grid-spot identification, tracking, radial scaling, and fine-alignment through cross-correlation.