 |
Journal articles
Shear measurement bias: II. A fast machine-learning calibration method
Abstract : 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 beyond Euclid requirements for a signal-to-noise ratio > 20 within ∼15 CPU hours of training using only ∼105 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.
Cited literature [46 references]
https://hal-cea.archives-ouvertes.fr/cea-03015651
Contributor : Edp Sciences <>
Submitted on : Friday, November 20, 2020 - 1:01:26 AM
Last modification on : Tuesday, December 8, 2020 - 3:42:34 AM
Contributor : Edp Sciences <>
Submitted on : Friday, November 20, 2020 - 1:01:26 AM
Last modification on : Tuesday, December 8, 2020 - 3:42:34 AM
File
aa38658-20.pdf
Publication funded by an institution