arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1707.01285astro-ph.CO
收藏

剪切测量偏差I:方法、模拟参数和测量参数的依赖性

Shear measurement bias I: dependencies on methods, simulation parameters and measured parameters

Arnau Pujol, Florent Sureau, Jerome Bobin, Frederic Courbin, Marc Gentile, Martin Kilbinger

摘要:

我们提出了一个研究的剪切偏置的依赖关系的模拟(输入)和测量(输出)参数,噪声,点扩散函数各向异性,像素大小,和模型偏差来自两个不同的和独立的星系形状估计。我们使用基于GREAT3控制空间常数分支的Galsim模拟图像,并通过模型拟合方法(gFIT)和基于矩的方法(Kaiser-Squires-Broadhurst)测量剪切偏差。我们发现这两种方法的输入和输出参数的偏差依赖关系,我们确定的主要依赖关系和原因。大多数结果是一致的两个估计,一个有趣的结果给出的方法的差异。我们还发现重要的依赖方向和形态的性质,如流量,大小和椭圆度。我们发现,噪声和像素化的输出属性和星系方向的偏差依赖性中发挥了重要作用。我们展示了一些模型偏差的例子,产生的Sersic指数n的偏差依赖,以及不同的剪切偏置组成的一个单一的Sersic配置文件和星系与光盘和凸起的星系之间的星系。我们还看到了一个重要的耦合偏置依赖的几个属性之间。正因为如此,我们需要同时研究几个测量的属性,以正确理解剪切偏置的性质。本文作为第一步的配套文件,描述了一种机器学习方法来建模剪切偏置作为一个复杂的功能,许多观察到的属性。

英文摘要:

We present a study of the dependencies of shear bias on simulation (input) and measured (output) parameters, noise, point-spread function anisotropy, pixel size, and the model bias coming from two different and independent galaxy shape estimators. We used simulated images from Galsim based on the GREAT3 control-space-constant branch, and we measured shear bias from a model-fitting method (gFIT) and a moment-based method (Kaiser-Squires-Broadhurst). We show the bias dependencies found on input and output parameters for both methods, and we identify the main dependencies and causes. Most of the results are consistent between the two estimators, an interesting result given the differences of the methods. We also find important dependences on orientation and morphology properties such as flux, size, and ellipticity. We show that noise and pixelization play an important role in the bias dependencies on the output properties and galaxy orientation. We show some examples of model bias that produce a bias dependence on the Sersic index n as well as a different shear bias between galaxies consisting of a single Sersic profile and galaxies with a disc and a bulge. We also see an important coupling between several properties on the bias dependences. Because of this, we need to study several measured properties simultaneously in order to properly understand the nature of shear bias. This paper serves as a first step towards a companion paper that describes a machine learning approach to modelling shear bias as a complex function of many observed properties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑