论文标题
使用深度学习连接低红移星系的光学形态，环境和HI质量分数
Connecting optical morphology, environment, and HI mass fraction for low-redshift galaxies using deep learning
论文作者
论文摘要
银河系的形态特征编码有关其气体含量，恒星形成历史和反馈过程的详细信息，这些详细信息在调节其生长和进化方面起着重要作用。我们使用深层卷积神经网络（CNN）来学习星系的光学形态信息，以估算直接从SDSS $ GRI $图像切口的中性原子氢（HI）含量。我们能够通过训练Alfalfa 40％样本中的Galaxies训练CNN，可以准确预测Galaxy的对数HI质量分数，$ \ MATHCAL {M} \ equiv \ log（m _ {\ rm hi}/m_ \ star）$。使用模式识别（PR），我们删除了带有不可靠的$ \ Mathcal {M} $估算的星系。我们测试了CNN对100％，XGASS和NIBLES目录的CNN预测，发现CNN始终优于先前的估计器。 CNN学到的HiMorphology连接在低至中密度的星系环境中似乎是恒定的，但在最高密度的环境中会分解。我们还使用可视化算法，梯度加权类激活图（GRAD-CAM）来确定哪些形态特征与低气体含量或高气体含量有关。这些结果表明，CNN是理解光学形态与其他属性之间的连接以及以定量和可解释方式探测其他变量的强大工具。
A galaxy's morphological features encode details about its gas content, star formation history, and feedback processes, which play important roles in regulating its growth and evolution. We use deep convolutional neural networks (CNNs) to learn a galaxy's optical morphological information in order to estimate its neutral atomic hydrogen (HI) content directly from SDSS $gri$ image cutouts. We are able to accurately predict a galaxy's logarithmic HI mass fraction, $\mathcal{M} \equiv \log(M_{\rm HI}/M_\star)$, by training a CNN on galaxies in the ALFALFA 40% sample. Using pattern recognition (PR), we remove galaxies with unreliable $\mathcal{M}$ estimates. We test CNN predictions on the ALFALFA 100%, xGASS, and NIBLES catalogs, and find that the CNN consistently outperforms previous estimators. The HI-morphology connection learned by the CNN appears to be constant in low- to intermediate-density galaxy environments, but it breaks down in the highest-density environments. We also use a visualization algorithm, Gradient-weighted Class Activation Maps (Grad-CAM), to determine which morphological features are associated with low or high gas content. These results demonstrate that CNNs are powerful tools for understanding the connections between optical morphology and other properties, as well as for probing other variables, in a quantitative and interpretable manner.
