摘要：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 GREAT3 data, we obtain a residual bias after the calibration compatible with 0 and beyondEuclidrequirements for a signal-to-noise ratio > 20 within ∼15 CPU hours of training using only ∼105objects. 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 asEuclidor theVera C. RubinObservatory, with fewer than a million images to simulate to learn the calibration function.
关键词：engravitational lensing: weakmethods: numericalmethods: data analysismethods: observationalmethods: statisticalcosmology: observations
