Empirical Contrast Model for High-Contrast Imaging -- A VLT/SPHERE Case Study

TL;DR

This paper presents an empirical contrast prediction model for high-contrast imaging with VLT/SPHERE, enabling better scheduling and quality control by accurately predicting raw contrast from archival data.

Contribution

The study develops and tests a physically motivated empirical contrast model trained on five years of archival data, achieving state-of-the-art accuracy for real-time observatory use.

Findings

Achieved a mean test error of 0.13 magnitudes at 300 mas.

Model performance is comparable to existing contrast models in the literature.

Best performance for targets with G-band magnitudes between 5 and 9.

Abstract

The ability to accurately predict the contrast achieved from high contrast imagers is important for efficient scheduling and quality control measures in modern observatories. We aim to consistently predict and measure the raw contrast achieved by SPHERE/IRDIS on a frame by frame basis to improve the efficiency and scientific yield with SPHERE at the Very Large Telescope (VLT).Contrast curves were calculated for over 5 years of archival data using the most common SPHERE/IRDIS coronagraphic mode in the H2/H3 dual band filter, consisting of approximately 80,000 individual frames. These were merged and interpolated with atmospheric data to create a large data-base of contrast curves with associated features. An empirical power law model for contrast, motivated by physical considerations, was then trained and finally tested on an out-of-sample test data set. At an angular separation of 300 mas, the contrast model achieved a mean (out-of-sample) test error of 0.13 magnitudes with the residual 5-95% percentiles between -0.23 and 0.64 magnitude respectively. The models test set root mean square error (RMSE) between 250-600 mas was between 0.31 - 0.40 magnitudes which is equivalent with other state-of-the-art contrast models presented in the literature. In general, the model performed best for targets between 5-9 G-band magnitude, with degraded performance for targets outside this range. This model is currently being incorporated into the Paranal SCUBA software for first level quality control and real time scheduling support.