论文标题
使用机器学习来确定$ z <1 $ agn主机星系的形态
Using Machine Learning to Determine Morphologies of $z<1$ AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey
论文作者
论文摘要
我们提出了一个机器学习框架，以准确表征活性银河核（AGN）宿主星系内$ z <1 $的形态。我们首先使用PSFGAN从中央点源将宿主星系光解次，然后调用Galaxy形态网络（Gamornet）来估计宿主星系是磁盘为主导，以凸起为主导的，或不确定的。使用来自HSC广泛调查的五个频段的光学图像，我们以三个红移箱独立构建模型：低$（0 <z <0.25）$，中$（0.25 <z <0.5）$和高$（0.5 <z <z <1.0）$。 By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for $\sim$ $60\%-70\%$ host galaxies from test sets, with a classification precision of $\sim$ $80\%-95\%$, depending on redshift bin.具体而言，我们的模型达到了$ 96 \％/82 \％/79 \％$的磁盘精度和$ 90 \％/90 \％/90 \％/80 \％$的凸起精度（对于3个红移垃圾箱），以对应于不明确分数的阈值，与$ 30 \％/43 \％/43 \％/42 \％/42 $ $。我们模型的分类精度对宿主星系半径和幅度具有明显的依赖性。对比比没有强大的依赖性。比较真实AGN的分类，我们的模型与传统的2D拟合与Galfit相符。 PSFGAN+GAGORNET框架不取决于拟合函数或与星系相关的输入参数的选择，比GalFit快运行的数量级，并且可以通过转移学习易于推广，从而使其成为研究AGN宿主Galaxy Galaxy形态的理想工具。
We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low $(0<z<0.25)$, medium $(0.25<z<0.5)$, and high $(0.5<z<1.0)$. By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for $\sim$ $60\%-70\%$ host galaxies from test sets, with a classification precision of $\sim$ $80\%-95\%$, depending on redshift bin. Specifically, our models achieve disk precision of $96\%/82\%/79\%$ and bulge precision of $90\%/90\%/80\%$ (for the 3 redshift bins), at thresholds corresponding to indeterminate fractions of $30\%/43\%/42\%$. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+GaMorNet framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging survey.
