http://arxiv.org/abs/2301.02986
Weak gravitational lensing is one of the most important probes of the nature of dark matter and dark energy. In order to extract cosmological information from next-generation weak lensing surveys (e.g., Euclid, Roman, LSST, and CSST) as much as possible, accurate measurements of weak lensing shear are required. In this work, we present a fully deep-learning-based approach to measuring weak lensing shear accurately. Our approach comprises two modules. The first one contains a CNN with two branches for taking galaxy images and PSF simultaneously, and the output of this module includes the galaxy’s magnitude, size, and shape. The second module includes a multiple-layer Neural Network to calibrate weak lensing shear measurements. We name the program Forklens and make it publicly available online. Applying Forklens to CSST-like mock images, we achieve consistent accuracy with traditional approaches (such as moment-based measurement and forward model fitting) on the sources with high signal-to-noise ratios (S/N). For the sources with meagre S/N, Forklens exhibits powerful latent denoising ability and offers accurate predictions on galaxy shapes. The final shear measurements with Forklens deliver a multiplicative bias $m=-0.4\pm3.0\times10^{-3}$ and an additive bias $c=-0.5\pm1.9\times10^{-4}$. Our tests with CSST-like simulation show that Forklens is competitive with other shear measurement algorithms such as Metacalibration, while Forklens can potentially lower the S/N limit. Moreover, the whole procedure of Forklens is automated and costs about 0.6 milliseconds per galaxy, which is appropriate to adequately take advantage of the sky coverage and depth of the upcoming weak lensing surveys.
Z. Zhang, H. Shan, N. Li, et. al.
Tue, 10 Jan 23
61/93
Comments: 11 pages, 11 figures; Comments are welcome!
You must be logged in to post a comment.