基于迁移学习的SDSS多波段图像聚类质量估计
Estimating Cluster Masses from SDSS Multi-band Images with Transfer Learning
摘要：
星系团的总质量表征了天体物理学和宇宙学的许多方面。对于众多的星系团来说，在大范围的红移和质量尺度上获得可靠和准确的质量估计是至关重要的。我们提出了一种迁移学习方法，使用SDSS数据发布12中的ugriz波段图像来估计集群质量。目标质量是从X射线或SZ测量得出的，这些测量仅适用于一小部分簇。我们设计了一个由两个卷积神经网络组成的半监督深度学习模型。在第一个网络中，特征提取器被训练来分类SDSS光度带。第二个网络将先前训练的特征作为输入，以估计它们的总质量。这项工作中的训练和测试过程完全依赖于真实的观测数据。我们的算法达到了平均绝对误差（MAE）为0.232 dex的平均和0.214 dex的最佳倍。性能与redMapPer给出的性能相当，0.192 dex。我们进一步应用联合集成梯度和类激活映射方法来解释这样的两步神经网络。随着训练数据集大小的增加，我们算法的性能可能会提高。这个概念验证实验证明了深度学习在最大限度地提高当前和未来大型聚类调查的科学回报方面的潜力。
英文摘要：
The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerous galaxy clusters over a wide range of redshifts and mass scales. We present a transfer-learning approach to estimate cluster masses using the ugriz-band images in the SDSS Data Release 12. The target masses are derived from X-ray or SZ measurements that are only available for a small subset of the clusters. We designed a semi-supervised deep learning model consisting of two convolutional neural networks. In the first network, a feature extractor is trained to classify the SDSS photometric bands. The second network takes the previously trained features as inputs to estimate their total masses. The training and testing processes in this work depend purely on real observational data. Our algorithm reaches a mean absolute error (MAE) of 0.232 dex on average and 0.214 dex for the best fold. The performance is comparable to that given by redMaPPer, 0.192 dex. We have further applied a joint integrated gradient and class activation mapping method to interpret such a two-step neural network. The performance of our algorithm is likely to improve as the size of training dataset increases. This proof-of-concept experiment demonstrates the potential of deep learning in maximizing the scientific return of the current and future large cluster surveys.
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