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The ability to accurately predict the contrast achieved from high contrastimagers is important for efficient scheduling and quality control measures inmodern observatories. We aim to consistently predict and measure the rawcontrast achieved by SPHERE/IRDIS on a frame by frame basis to improve theefficiency and scientific yield with SPHERE at the Very Large Telescope(VLT).Contrast curves were calculated for over 5 years of archival data usingthe most common SPHERE/IRDIS coronagraphic mode in the H2/H3 dual band filter,consisting of approximately 80,000 individual frames. These were merged andinterpolated with atmospheric data to create a large data-base of contrastcurves with associated features. An empirical power law model for contrast,motivated by physical considerations, was then trained and finally tested on anout-of-sample test data set. At an angular separation of 300 mas, the contrastmodel achieved a mean (out-of-sample) test error of 0.13 magnitudes with theresidual 5-95% percentiles between -0.23 and 0.64 magnitude respectively. Themodels test set root mean square error (RMSE) between 250-600 mas was between0.31 - 0.40 magnitudes which is equivalent with other state-of-the-art contrastmodels presented in the literature. In general, the model performed best fortargets between 5-9 G-band magnitude, with degraded performance for targetsoutside this range. This model is currently being incorporated into the ParanalSCUBA software for first level quality control and real time schedulingsupport.

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