Speckle Suppression with the Project 1640 Integral Field Spectrograph
Justin R. Crepp, Laurent Pueyo, Douglas Brenner, Ben R. Oppenheimer,, Neil Zimmerman, Sasha Hinkley, Ian Parry, David King, Gautam Vasisht, Charles, Beichman, Lynne Hillenbrand, Richard Dekany, Mike Shao, Rick Burruss, Lewis, C. Roberts Jr., Antonin Bouchez, Jenny Roberts

TL;DR
This paper presents advanced speckle suppression techniques for the Project 1640 instrument, significantly improving its ability to detect faint exoplanets and circumstellar material by reducing residual noise close to stars.
Contribution
The paper introduces a novel speckle suppression pipeline combining the IFS's chromatic diversity with the LOCI algorithm, achieving over an order of magnitude noise reduction in high-contrast imaging.
Findings
Achieved $5\sigma$ contrast of $2.1\times10^{-5}$ at 1 arcsecond in 20 minutes.
Demonstrated suppression factors of at least tenfold in on-sky data.
Projected near-infrared contrast levels of about $10^{-7}$ at subarcsecond separations.
Abstract
Project 1640 is a high-contrast imaging instrument recently commissioned at Palomar observatory. A combination of a coronagraph with an integral field spectrograph (IFS), Project 1640 is designed to detect and characterize extrasolar planets, brown dwarfs, and circumstellar material orbiting nearby stars. In this paper, we present our data processing techniques for improving upon instrument raw sensitivity via the removal of quasi-static speckles. Our approach utilizes the chromatic image diversity provided by the IFS in combination with the locally-optimized combination of images (LOCI) algorithm to suppress the intensity of residual contaminating light in close angular proximity to target stars. We describe the Project 1640 speckle suppression pipeline (PSSP) and demonstrate the ability to detect companions with brightness comparable to and below that of initial speckle intensities…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
