arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 0709.1359astro-ph
收藏

基于有限图像的支持向量机对高红移星系的鲁棒形态学分类。一、方法说明

A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description

M. Huertas-Company, D. Rouan, L. Tasca, G. Soucail, O. Le Fevre

摘要:

我们提出了一种新的非参数方法来量化的星系形态的基础上,一个特定的家庭学习机称为支持向量机。该方法,可以被看作是一个推广的经典CAS分类,但具有无限数量的维度和非线性边界之间的决策区域,是完全自动化的,因此特别适合于大型宇宙学调查。源代码可在http://www.lesia.obspm.fr/~huertas/galsvm.html下载。为了测试该方法,我们使用了在CFHT用WIRCam观察到的可见度有限的近红外($K_s$波段,$2,16μm$)样本,其中值红移为$z\sim0.8$。该机器使用模拟样本进行训练,该模拟样本由来自SDSS的局部视觉分类样本构建,该SDSS在高红移样本的静止帧(i波段,$0.77μm$)中选择,并人工红移以匹配观察条件。我们使用一个12维的体积,包括5个形态参数和其他星系的特性,如光度和红移。我们表明,在两个主要的形态类型(晚型和早期型)的定性分离,可以得到一个低于20%的误差的完整性限制的样品($KAB\sim 22$),这是2倍以上,更好的是什么会得到一个经典的C/A分类相同的样品,并确实可比空间数据。该方法经过优化以解决特定问题,提供客观和自动的误差估计,可以与其他调查进行直接比较。

英文摘要:

We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS classification but with an unlimited number of dimensions and non-linear boundaries between decision regions, is fully automated and thus particularly well adapted to large cosmological surveys. The source code is available for download at http://www.lesia.obspm.fr/~huertas/galsvm.html To test the method, we use a seeing limited near-infrared ($K_s$ band, $2,16μm$) sample observed with WIRCam at CFHT at a median redshift of $z\sim0.8$. The machine is trained with a simulated sample built from a local visually classified sample from the SDSS chosen in the high-redshift sample's rest-frame (i band, $0.77μm$) and artificially redshifted to match the observing conditions. We use a 12-dimensional volume, including 5 morphological parameters and other caracteristics of galaxies such as luminosity and redshift. We show that a qualitative separation in two main morphological types (late type and early type) can be obtained with an error lower than 20% up to the completeness limit of the sample ($KAB\sim 22$) which is more than 2 times better that what would be obtained with a classical C/A classification on the same sample and indeed comparable to space data. The method is optimized to solve a specific problem, offering an objective and automated estimate of errors that enables a straightforward comparison with other surveys.

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。

↑