Improving Signal to Noise in the Direct Imaging of Exoplanets and Circumstellar Disks
Zahed Wahhaj, Lucas A. Cieza, Dimitri Mawet, Bin Yang, Hector Canovas,, Jos De Boer, Simon Casassus, Francois Menard, Matthias R. Schreiber, Michael, C. Liu, Beth A. Biller, Eric L. Nielsen, and Thomas L. Hayward

TL;DR
This paper introduces MLOCI, a new algorithm that significantly enhances the signal-to-noise ratio in direct imaging of exoplanets and disks, without increasing false detections, by optimally combining images based on the LOCI framework.
Contribution
The paper presents MLOCI, a novel image processing algorithm that improves detection sensitivity in exoplanet imaging by optimizing image combinations without prior source location knowledge.
Findings
MLOCI improves SNR of point sources by 30-400%.
No increase in false detection rates with MLOCI.
Applicable to LOCI and PCA methods, with caution on false positives.
Abstract
We present a new algorithm designed to improve the signal to noise ratio (SNR) of point and extended source detections in direct imaging data. The novel part of our method is that it finds the linear combination of the science images that best match counterpart images with signal removed from suspected source regions. The algorithm, based on the Locally Optimized Combination of Images (LOCI) method, is called Matched LOCI or MLOCI. We show using data obtained with the Gemini Planet Imager (GPI) and Near-Infrared Coronagraphic Imager (NICI) that the new algorithm can improve the SNR of point source detections by 30-400% over past methods. We also find no increase in false detections rates. No prior knowledge of candidate companion locations is required to use MLOCI. While non-blind applications may yield linear combinations of science images which seem to increase the SNR of true sources…
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