论文标题
从具有混合卷积神经网络图像的图像预测星系光谱
Predicting galaxy spectra from images with hybrid convolutional neural networks
论文作者
论文摘要
可以通过其光谱的特征（例如氧发射线或形态学特征，例如螺旋臂）来描述星系。尽管光谱法提供了对控制星系演化的物理过程的丰富描述，但光谱数据在观察方面却昂贵。我们第一次能够直接从宽带成像中直接预测星系光谱。我们使用具有反卷积的混合卷积神经网络而不是批处理归一化提出了一种强大的新方法。这种混合CNN在我们的测试中优于其他模型。对于未来的宽场调查，例如Vera C. Rubin天文台和Nancy Grace Roman Roman Space望远镜，用于未来的宽场调查，学习的映射将具有变革性的变化，这是通过乘以光谱型有限的星系样品的科学回报。
Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms. Although spectroscopy provides a rich description of the physical processes that govern galaxy evolution, spectroscopic data are observationally expensive to obtain. For the first time, we are able to robustly predict galaxy spectra directly from broad-band imaging. We present a powerful new approach using a hybrid convolutional neural network with deconvolution instead of batch normalization; this hybrid CNN outperforms other models in our tests. The learned mapping between galaxy imaging and spectra will be transformative for future wide-field surveys, such as with the Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope, by multiplying the scientific returns for spectroscopically-limited galaxy samples.
