Shear measurement bias II: a fast machine learning calibration method

Understand

We present a new shear calibration method based on machine learning.

• The method estimates the individual shear responses of the objects from the combination of several measured properties on the images using supervised learning.
• The supervised learning uses the true individual shear responses obtained from copies of the image simulations with different shear values.
• On simulated GREAT3data, we obtain a residual bias after the calibration compatible with 0 and beyond Euclid requirements for a signal-to-noise ratio > 20 within ~15 CPU hours of training using only ~10^5 objects.