Confidence Level and Sensitivity Limits in High Contrast Imaging

TL;DR

This paper analyzes speckle noise in high contrast imaging, demonstrating its statistical properties, and introduces a method to determine detection thresholds for exoplanet imaging with confidence levels, accounting for non-Gaussian noise.

Contribution

It reveals the spatial-temporal equivalence of speckle intensity distributions and develops a technique to accurately set detection thresholds considering speckle noise statistics.

Findings

Speckle intensity follows a modified Rician distribution.

Detection thresholds need to be up to three times higher than Gaussian assumptions for same confidence.

Angular differential imaging produces quasi-Gaussian residuals, improving detection confidence.

Abstract

In long adaptive optics corrected exposures, exoplanet detections are currently limited by speckle noise originating from the telescope and instrument optics, and it is expected that such noise will also limit future high-contrast imaging instruments for both ground and space-based telescopes. Previous theoretical analysis have shown that the time intensity variations of a single speckle follows a modified Rician. It is first demonstrated here that for a circular pupil this temporal intensity distribution also represents the speckle spatial intensity distribution at a fix separation from the point spread function center; this fact is demonstrated using numerical simulations for coronagraphic and non-coronagraphic data. The real statistical distribution of the noise needs to be taken into account explicitly when selecting a detection threshold appropriate for some desired confidence level. In this paper, a technique is described to obtain the pixel intensity distribution of an image and its corresponding confidence level as a function of the detection threshold. Using numerical simulations, it is shown that in the presence of speckles noise, a detection threshold up to three times higher is required to obtain a confidence level equivalent to that at 5sigma for Gaussian noise. The technique is then tested using TRIDENT CFHT and angular differential imaging NIRI Gemini adaptive optics data. It is found that the angular differential imaging technique produces quasi-Gaussian residuals, a remarkable result compared to classical adaptive optic imaging. A power-law is finally derived to predict the 1-3*10^-7 confidence level detection threshold when averaging a partially correlated non-Gaussian noise.