基于深度学习的5个CANDELS天区类视觉形态星表
星系天体物理
2015-11-04 v1 宇宙学与河外天体物理
摘要
我们呈现了一份5个CANDELS天区(GOODS-N, GOODS-S, UDS, EGS和COSMOS)中个星系()的类视觉H波段形态星表。形态由卷积神经网络(ConvNets)估算。样本的中位红移为。该算法在具有公开视觉分类的GOODS-S上训练，然后应用于其他4个天区。遵循CANDELS主要形态分类方案，我们的模型给出了每个星系具有球状体、盘、呈现不规则性、致密或点源以及不可分类的概率。ConvNets能够给定星系图像以零偏差和散射预测投票比例。误分类比例小于。我们的分类方案相对于基于CAS(集中度-不对称性-平滑度)的方法是一个重大改进，后者在高红移处达到污染极限。该星表随本文通过发布。
引用
@article{arxiv.1509.05429,
title = {A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning},
author = {M. Huertas-Company and R. Gravet and G. Cabrera-Vives and P. G. Pérez-González and J. S. Kartaltepe and G. Barro and M. Bernardi and S. Mei and F. Shankar and P. Dimauro and E. F. Bell and D. Kocevski and D. C. Koo and S. M. Faber and D. H. Mcintosh},
journal= {arXiv preprint arXiv:1509.05429},
year = {2015}
}
备注
Accepted for publication in ApjS. Figure 10 summarizes the excellent agreement between our classification and a pure visual one. Table 3 shows the content of the catalogs. The catalogs are available from the Rainbow database (http://rainbowx.fis.ucm.es/Rainbow_navigator_public) based on the selections from the CANDELS team and cross-matched with 3D-HST v4.1 catalogs