首页> 外文期刊>Astronomische Nachrichten: A Journal on all Fields of Astronomy >GaBoDS: The Garching-Bonn Deep Survey - IV. Methods for the image reduction of multi-chip cameras demonstrated on data from the ESO wide-field imager
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GaBoDS: The Garching-Bonn Deep Survey - IV. Methods for the image reduction of multi-chip cameras demonstrated on data from the ESO wide-field imager

机译:GaBoDS:Garching-Bonn深入调查-IV。在ESO宽场成像仪的数据上演示了多芯片相机的图像缩小方法

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We present our image processing system for the reduction of optical imaging data from multi-chip cameras. In the framework of the Garching Bonn Deep Survey (GaBoDS; Schirmer et al. 2003) consisting of about 20 square degrees of high-quality data from WFI@MPG/ESO 2.2m, our group developed an imaging pipeline for the homogeneous and efficient processing of this large data set. Having weak gravitational lensing as the main science driver, our algorithms are optimised to produce deep co-added mosaics from individual exposures obtained from empty field observations. However, the modular design of our pipeline allows an easy adaption to different scientific applications. Our system has already been ported to a large variety of optical instruments and its products have been used in various scientific contexts. In this paper we give a thorough description of the algorithms used and a careful evaluation of the accuracies reached. This concerns the removal of the instrumental signature, the astrometric alignment, photometric calibration and the characterisation of final co-added mosaics. In addition we give a more general overview on the image reduction process and comment on observing strategies where they have significant influence on the data quality.
机译:我们介绍了用于减少来自多芯片相机的光学成像数据的图像处理系统。在Garching波恩深层调查(GaBoDS; Schirmer et al。2003)的框架中,该组包含来自WFI @ MPG / ESO 2.2m的约20平方度的高质量数据,我们小组开发了一种成像管道,用于均匀有效的处理这个大数据集。由于弱引力透镜是主要的科学驱动因素,因此我们的算法经过优化,可以根据从空场观测中获得的单个曝光结果来生成深的共添加马赛克。但是,我们管道的模块化设计可以轻松适应不同的科学应用。我们的系统已经被移植到各种各样的光学仪器上,其产品已在各种科学环境中使用。在本文中,我们对使用的算法进行了详尽的描述,并对达到的准确性进行了仔细的评估。这涉及仪器签名的去除,天体对准,光度校准和最终共添加马赛克的特征。此外,我们对图像缩小过程进行了更一般的概述,并评论了对数据质量有重大影响的观察策略。

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