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Single-Epoch Supernova Classification with Deep Convolutional Neural Networks

机译：深度卷积神经网络的单历次超新星分类

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Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large samples of SNeIa and investigating their detailed characteristics have become an important issue in cosmology and astronomy. The current photometric supernova surveys produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods. Existing methods relied on a photometric approach that first measures the luminance of supernova candidates precisely and then fits the results to a parametric function of temporal changes in luminance. However, it inevitably requires multi-epoch observations and complex luminance measurements. In this work, we present a novel method for classifying SNeIa simply from single-epoch observation images without any complex measurements, by effectively integrating the state-of-the-art computer vision methodology into the standard photometric approach. Our method first builds a convolutional neural network for estimating the luminance of supernovae from telescope images, and then constructs another neural network for the classification, where the estimated luminances and observation dates are used as features for classification. Both of the neural networks are integrated into a single deep neural network to classify SNeIa directly from observation images. Experimental results show the effectiveness of the proposed method and reveal classification performance comparable to existing photometric methods with multi-epoch observations.
机译：超新星Ia（SNeIa）在探索宇宙膨胀的历史中起着重要作用，因为它们是最著名的标准蜡烛，我们可以用它精确地测量到物体的距离。在宇宙学和天文学中，寻找大尺度的星云样本并研究其详细特征已经成为一个重要的问题。当前的光度超新星调查产生的候选物远远超出了光谱学所能追踪的数量，从而突出了对有效分类方法的需求。现有方法依赖于光度学方法，该方法首先精确地测量超新星候选者的亮度，然后将结果拟合为亮度随时间变化的参数函数。但是，它不可避免地需要多时间观测和复杂的亮度测量。在这项工作中，我们提出了一种通过有效地将最新的计算机视觉方法集成到标准光度学方法中，仅从单历时的观察图像中对SNeIa进行分类的新颖方法，而无需进行任何复杂的测量。我们的方法首先建立了一个卷积神经网络，用于从望远镜图像估计超新星的亮度，然后构造另一个神经网络进行分类，其中将估计的亮度和观测日期用作分类的特征。这两个神经网络都集成到单个深度神经网络中，可以直接从观察图像中对SNeIa进行分类。实验结果证明了该方法的有效性，并揭示了与具有多历时观测结果的现有光度法相比可比的分类性能。

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