论文标题
开放超新星目录中IA型超新星的光度数据驱动分类
Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog
论文作者
论文摘要
我们提出了一种使用光度信息对IA型超新星型的基于机器学习的检测的新方法。与其他方法不同，在训练过程中仅使用实际观察数据。尽管接受了相对较小的样本训练，但该方法在开放超新星目录的实际数据上显示出良好的结果。我们还调查了从ParatireC模拟训练数据集到实际数据应用程序的模型转移，并相反，并发现两种情况下的性能都显着降低，从而突出了模拟和真实数据之间的现有差异。
We propose a novel approach for a machine-learning-based detection of the type Ia supernovae using photometric information. Unlike other approaches, only real observation data is used during training. Despite being trained on a relatively small sample, the method shows good results on real data from the Open Supernovae Catalog. We also investigate model transfer from the PLAsTiCC simulations train dataset to real data application, and the reverse, and find the performance significantly decreases in both cases, highlighting the existing differences between simulated and real data.
