Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys
Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys
复制标题
DESI 传统成像巡天中星系的光度红移估计
DOI：
10.1093/mnras/stac3037
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发表时间：
2022-11
影响因子：
4.8
通讯作者：
Sisi Yang
中科院分区：
文献类型：
--
作者：
Changhua Li;Yanxia Zhang;Chenzhou Cui;Dongwei Fan;Yongheng Zhao;Xue-Bing Wu;Jing-Yi Zhang;Yihan Tao;Jun Han;Yunfei Xu;Shanshan Li;Linying Mi;Boliang He;Zihan Kang;Youfen Wang;Hanxi Yang;Sisi Yang
摘要。光度红移的准确估计对于实现大型测量项目的科学目标起着至关重要的作用。模板拟合和机器学习是目前应用的两种主要方法。基于对DESI Legacy Imaging Surveys DR 9星系表和SDSS DR 16星系表进行交叉相关得到的训练集，分别使用了eazy模板拟合方法和catboost机器学习方法，并对这两种方法进行了优化。然后，用DESI Legacy Imaging Surveys DR 9星系表与LAMOST DR 7、GAMA DR 3和WiggleZ星系表的交叉匹配样本对模型进行了检验。此外，还比较了三种机器学习方法（catboost、多层感知器和随机森林）; catboost在我们的案例中显示了其优越性。通过特征选择和模型参数的优化，catboost算法能够获得更高的精度，在g ≤ 24.0，r ≤ 23.4，z ≤ 22.5的条件下，catboost算法的性能最好（rmMSE =0.0032$，σNMAD = 0.0156，$O=0.88{{rmpercent}}$）。但对于高红移星系，尤其是在训练样本的红移范围之外的星系，eazy可以提供更精确的光度红移估计。最后，我们利用catboost和eazy完成了对所有DESI Legacy Imaging Survey DR 9星系的红移估计，这将有助于对星系及其性质的进一步研究。
ABSTRACT. The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. Template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and the SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as eazy for template-fitting approach and catboost for machine learning. Then, the created models are tested by the cross-matched samples of the DESI Legacy Imaging Surveys DR9 galaxy catalogue with LAMOST DR7, GAMA DR3, and WiggleZ galaxy catalogues. Moreover, three machine learning methods (catboost, Multi-Layer Perceptron, and Random Forest) are compared; catboost shows its superiority for our case. By feature selection and optimization of model parameters, catboost can obtain higher accuracy with optical and infrared photometric information, the best performance ($rm MSE=0.0032$, σNMAD = 0.0156, and $O=0.88{{ rm per cent}}$) with g ≤ 24.0, r ≤ 23.4, and z ≤ 22.5 is achieved. But eazy can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redshift range of training sample. Finally, we finish the redshift estimation of all DESI Legacy Imaging Surveys DR9 galaxies with catboost and eazy, which will contribute to the further study of galaxies and their properties.
DOI：
10.1111/j.1365-2125.2006.02665.x
发表时间：
2006
期刊：
--
影响因子：
--
作者：
A. N. Nicholson;A. W. Peck
通讯作者：
A. N. Nicholson;A. W. Peck