论文标题
深度学习附近的银河系奇特速度
Deep Learning nearby galaxy peculiar velocities
论文作者
论文摘要
我们探索如何使用附近星系图像中的信息来估计其距离。我们使用Illustris模拟的星系图像训练卷积神经网络（NN）进行此操作。我们表明，如果对NN进行了对数据的训练，并将其添加到真实距离中的随机误差（使用光谱红移而不是实际距离表示训练），则NN可以预测测试数据集中的距离，其精度比训练集更高。这并不罕见，因为NNS经常受到噪声增加的数据训练，以提高鲁棒性。但是，在这种情况下，它为估计附近星系的特殊速度提供了一条途径。鉴于具有已知光谱红移的星系可以使用NN预测的距离来估计特殊速度。尝试使用相对较低的分辨率（每个像素1.4 ARCSEC）模拟星系图像，我们发现与观察者的平均距离为75 MPC的星系的分数RMS距离误差为7.7％，导致RMS特征性速度误差为440 km/s。在同伴论文中，我们将该技术应用于NASA Sloan Atlas附近的145,115个星系。
We explore how information in images of nearby galaxies can be used to estimate their distance. We train a convolutional Neural Network (NN) to do this, using galaxy images from the Illustris simulation. We show that if the NN is trained on data with random errors added to the true distance (representing training using spectroscopic redshift instead of actual distance), then the NN can predict distances in a test dataset with greater accuracy than it was given in the training set. This is not unusual, as often NNs are trained on data with added noise, in order to increase robustness. In this case, however, it offers a route to estimating peculiar velocities of nearby galaxies. Given a galaxy with a known spectroscopic redshift one can use the NN-predicted distance to make an estimate of the peculiar velocity. Trying this using relatively low resolution (1.4 arcsec per pixel) simulated galaxy images we find fractional RMS distance errors of 7.7% for galaxies at a mean distance of 75 Mpc from the observer, leading to RMS peculiar velocity errors of 440 km/s. In a companion paper we apply the technique to 145,115 nearby galaxies from the NASA Sloan Atlas.
