Empirical contrast model for high-contrast imaging A VLT/SPHERE case study

Abstract

Context. The ability to accurately predict the contrast achieved with high-contrast imagers is important for efficient scheduling and quality control measures in modern observatories. Aims. We aim to consistently predict and measure the raw contrast achieved by SPHERE/IRDIS on a frame-by-frame basis in order to improve the efficiency of SPHERE at the Very Large Telescope (VLT) and maximise scientific yield. Methods. Contrast curves were calculated for over 5 yr of archival data obtained using the most common SPHERE/IRDIS corona-graphic mode in the H2/H3 dual-band filter. These data consist of approximately 80 000 individual frames, which were merged and interpolated with atmospheric data to create a large database 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 dataset. Results. At an angular separation of 300 mas, the contrast model achieved a mean (out-of-sample) test error of 0.13 magnitude with the 5th and 95th percentiles of the residuals equal to −0.23 and 0.64 magnitude respectively. The models test-set root mean square error (RMSE) between 250 and 600 mas was between 0.31 and 0.40 magnitude, which is equivalent to that of other state-of-the-art contrast models presented in the literature. In general, the model performed best for targets of between 5 and 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.