Shear measurement bias

II. A fast machine-learning calibration method

¹ DEDIP/DAP, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
e-mail: arnaupv@gmail.com
² AIM, CEA, CNRS, Université Paris-Saclay, Université Paris Diderot, Sorbonne Paris Cité, 91191 Gif-sur-Yvette, France
³ Institut d’Estudis Espacials de Catalunya (IEEC), 08034 Barcelona, Spain
⁴ Institute of Space Sciences (ICE, CSIC), 08193 Barcelona, Spain
⁵ Institute of Particle and Cosmos Physics (IPARCOS), Universidad Complutense de Madrid, 28040 Madrid, Spain
⁶ Institut d’Astrophysique de Paris, UMR7095 CNRS, Université Pierre & Marie Curie, 98bis boulevard Arago, 75014 Paris, France

Received: 15 June 2020
Accepted: 24 September 2020

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.