Fast point spread function modeling with deep learning

点扩散函数 弱引力透镜 计算机科学 物理 卷积神经网络 稳健主成分分析 主成分分析 蒙特卡罗方法 深度学习 人工智能 银河系 天体物理学 红移 数学 统计
作者
Jörg Herbel,Tomasz Kacprzak,A. Amara,Alexandre Réfrégier,Aurélien Lucchi
出处
期刊:Journal of Cosmology and Astroparticle Physics [Institute of Physics]
卷期号:2018 (07): 054-054 被引量:45
标识
DOI:10.1088/1475-7516/2018/07/054
摘要

Modeling the Point Spread Function (PSF) of wide-field surveys is vital for many astrophysical applications and cosmological probes including weak gravitational lensing. The PSF smears the image of any recorded object and therefore needs to be taken into account when inferring properties of galaxies from astronomical images. In the case of cosmic shear, the PSF is one of the dominant sources of systematic errors and must be treated carefully to avoid biases in cosmological parameters. Recently, forward modeling approaches to calibrate shear measurements within the Monte-Carlo Control Loops (MCCL) framework have been developed. These methods typically require simulating a large amount of wide-field images, thus, the simulations need to be very fast yet have realistic properties in key features such as the PSF pattern. Hence, such forward modeling approaches require a very flexible PSF model, which is quick to evaluate and whose parameters can be estimated reliably from survey data. We present a PSF model that meets these requirements based on a fast deep-learning method to estimate its free parameters. We demonstrate our approach on publicly available SDSS data. We extract the most important features of the SDSS sample via principal component analysis. Next, we construct our model based on perturbations of a fixed base profile, ensuring that it captures these features. We then train a Convolutional Neural Network to estimate the free parameters of the model from noisy images of the PSF. This allows us to render a model image of each star, which we compare to the SDSS stars to evaluate the performance of our method. We find that our approach is able to accurately reproduce the SDSS PSF at the pixel level, which, due to the speed of both the model evaluation and the parameter estimation, offers good prospects for incorporating our method into the MCCL framework.
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