  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. This efficient
machine-learning approach can use a smaller data set because the method avoids
the contribution from shape noise. The low dimensionality of the input data
also leads to simple neural network architectures. We compare it to the
recently described method Metacalibration, which shows similar performances.
The different methods and systematics suggest that the two methods are very
good complementary methods. Our method can therefore be applied without much
effort to any survey such as Euclid or the Vera C. Rubin Observatory, with
fewer than a million images to simulate to learn the calibration function.
