A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images I. Method description
A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images I. Method description
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DOI：
10.1051/0004-6361:20078625
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发表时间：
2007-09
影响因子：
6.5
通讯作者：
M. Huertas-Company;L. Tasca;D. Rouan;D. Pelat;J. Kneib;O. Fèvre;P. Capak;J. Kartaltepe;A. Koekemoer;H. McCracken;M. Salvato;D. Sanders;C. Willott
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作者：
M. Huertas-Company;L. Tasca;D. Rouan;D. Pelat;J. Kneib;O. Fèvre;P. Capak;J. Kartaltepe;A. Koekemoer;H. McCracken;M. Salvato;D. Sanders;C. Willott
内容：星系形态学是研究星系物理结构最容易的方法，但如何在星系演化的框架中解释它仍然是一个问题。它对波长的依赖性使得在局域和高红移群体之间进行比较变得困难。此外，测量形态的质量强烈依赖于图像分辨率，不同调查之间的比较也是一个问题。目的：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，这可以被看作是一个推广的经典C/A分类，但与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上下载。方法：为了测试该方法，我们使用了可见度有限的近红外（Ks波段，2，16？m）在CFHT用WIRCam在z ~ 0.8的中值红移处观察到的样品。该机器的训练与模拟样本建立从本地视觉分类样本从SDSS，选择在高红移样本的休息帧（i波段，0.77？m）并人为地红移以匹配观测条件。我们使用了一个12维的体积，包括5个形态参数，以及星系的其他特征，如光度和红移。模拟样本的一部分用于测试机器并评估其准确性。结果如下：我们表明，在两个主要的形态类型（晚型和早型）的定性分离，可以得到一个低于20%的误差，直到样本的完整性限制（KAB ~ 22），这是超过2倍以上，将获得与经典的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。在高红移样本静止坐标系中选取训练样本，可以使结果不受波长依赖的影响，从而更容易从演化的角度对其进行解释。根据加拿大-法国-夏威夷望远镜（CFHT）获得的观测结果，该望远镜由加拿大国家研究理事会、法国国家科学研究中心国家大学科学研究所和夏威夷大学操作。
Context: Morphology is the most accessible tracer of the physical structure of galaxies, but its interpretation in the framework of galaxy evolution still remains a problem. Its dependence on wavelength renders the comparison between local and high redshift populations difficult. Furthermore, the quality of the measured morphology being strongly dependent on the image resolution, the comparison between different surveys is also a problem. Aims: 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, which can be seen as a generalization of the classical C/A 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 Methods: To test the method, we use a seeing limited near-infrared (Ks band, 2,16 ?m) sample observed with WIRCam at CFHT at a median redshift of z ~ 0.8. 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, 0.77 ?m) and artificially redshifted to match the observing conditions. We use a 12-dimensional volume, including 5 morphological parameters, and other characteristics of galaxies such as luminosity and redshift. A fraction of the simulated sample is used to test the machine and assess its accuracy. Results: 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 ~ 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. Selecting the training sample in the high-redshift sample rest-frame makes the results free from wavelength dependent effects and hence its interpretation in terms of evolution easier. Based on observations obtained at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council of Canada, the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France, and the University of Hawaii.