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arXiv 2107.03589astro-ph.COastro-ph.IM
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用星系形态学表征测光红移概率密度函数的改进

Characterising Improvements in Photometric Redshift Probability Density Functions with Galaxy Morphology

John Y. H. Soo, Benjamin Joachimi

摘要:

在这项工作中,我们研究了星系形态对光度红移(照片- $z$)概率密度函数(PDF)的影响。通过包括星系形态参数,如半径,轴比,表面亮度和Sérsic指数,以及$ugriz$宽带作为输入参数,我们使用机器学习照片- $z$算法ANNz 2来训练和测试来自加拿大-法国-夏威夷望远镜条纹-82(CS 82)巡天的星系。对连续排序概率得分(CRPS)、概率积分变换(PIT)、贝叶斯几率参数、甚至PDF的宽度和高度等参数进行了评估,并比较了在训练过程中使用不同数量的输入参数时的结果。当星系形态被添加到训练中时,我们发现CRPS和PDF的宽度有所改善,并且改善更大,特别是当缺乏宽带数量时。

英文摘要:

In this work, we studied the impact of galaxy morphology on photometric redshift (photo-$z$) probability density functions (PDFs). By including galaxy morphological parameters like the radius, axis-ratio, surface brightness and the Sérsic index in addition to the $ugriz$ broadbands as input parameters, we used the machine learning photo-$z$ algorithm ANNz2 to train and test on galaxies from the Canada-France-Hawaii Telescope Stripe-82 (CS82) Survey. Metrics like the continuous ranked probability score (CRPS), probability integral transform (PIT), Bayesian odds parameter, and even the width and height of the PDFs were evaluated, and the results were compared when different number of input parameters were used during the training process. We find improvements in the CRPS and width of the PDFs when galaxy morphology has been added to the training, and the improvement is larger especially when the number of broadband magnitudes are lacking.

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。

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