The Giant Accreting Protoplanet Survey (GAPlanetS): Optimization Techniques for Robust Detections of Protoplanets
High-contrast imaging has afforded astronomers the opportunity to study light directly emitted by adolescent (tens of megayears) and "proto" (<10 Myr) planets still undergoing formation. Direct detection of these planets is enabled by empirical point-spread function (PSF) modeling and removal algorithms. The computational intensity of such algorithms, as well as their multiplicity of tunable input parameters, has led to the prevalence of ad hoc optimization approaches to high-contrast imaging results. In this work, we present a new, systematic approach to optimization vetted using data of the high-contrast stellar companion HD 142527 B from the Magellan Adaptive Optics Giant Accreting Protoplanet Survey (GAPlanetS). More specifically, we present a grid search technique designed to explore three influential parameters of the PSF subtraction algorithm pyKLIP: annuli, movement, and KL modes. We consider multiple metrics for postprocessed image quality in order to optimally recover at Hα (656 nm) synthetic planets injected into contemporaneous continuum (643 nm) images. These metrics include peak (single-pixel) signal-to-noise ratio (S/N), average (multipixel average) S/N, 5σ contrast, and false-positive fraction. We apply continuum-optimized KLIP reduction parameters to six Hα direct detections of the low-mass stellar companion HD 142527 B and recover the companion at a range of separations. Relative to a single-informed, nonoptimized set of KLIP parameters applied to all data sets uniformly, our multimetric grid search optimization led to improvements in companion S/N of up to 1.2σ, with an average improvement of 0.6σ. Since many direct imaging detections lie close to the canonical 5σ threshold, even such modest improvements may result in higher yields in future imaging surveys.
© 2023. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. K.B.F., W.O.B., and J.A. acknowledge funding from NSF-AST-2009816. L.M.C.'s work was supported by NASA Exoplanets Research Program (XRP) grants 80NSSC18K0441 and 80NSSC21K0397. W.O.B. thanks the LSSTC Data Science Fellowship Program, which is funded by LSSTC, NSF Cybertraining grant No. 1829740, the Brinson Foundation, and the Moore Foundation; their participation in the program has benefited this work. K.M.M.'s work has been supported by the NASA XRP by cooperative agreement NNX16AD44G. This paper includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.
Published - Adams_Redai_2023_AJ_165_57.pdf