Optimizing a magnitude-limited spectroscopic training sample for photometric classification of supernovae
Optimizing a magnitude-limited spectroscopic training sample for photometric classification of supernovae
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DOI：
10.1093/mnras/stab2343
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发表时间：
2020-12
影响因子：
4.8
通讯作者：
J. Carrick;I. Hook;E. Swann;Kyle Boone;C. Frohmaier;A. Kim;M. Sullivan
中科院分区：
文献类型：
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作者：
J. Carrick;I. Hook;E. Swann;Kyle Boone;C. Frohmaier;A. Kim;M. Sullivan
为了准备对来自传统空间和时间调查(LSST)的瞬变进行光度分类，我们使用不同的训练数据集进行测试。利用4米多目标光谱望远镜(4MOST)时间域河外探测(TETES)可以对瞬变进行分类的深度估计，我们模拟了一个星等限制的样本，达到RAB≈22.5MAG。我们使用软件SNMachine运行我们的模拟，这是一个使用机器学习的光度分类管道。与典型的训练样本相比，当训练样本数量有限时，机器学习算法很难对超新星进行分类。当我们将大小受限的训练样本与从大型光谱设备上观测到的微弱的高红移超新星的模拟真实样本相结合时，分类性能显著提高；对于四种算法中的两种，算法的接收器算子特征曲线下的平均面积在10次以上的范围从0.547-0.628增加到0.946-0.969，分类样本的纯度在所有次都达到95%。通过使用增强软件avocado创建新的人造光照曲线，我们在针对所有机器学习算法执行的所有10次运行中，实现了95%的分类样本纯度。使用人工神经网络算法，我们也达到了最高的AUC值0.986。拥有“真正的”微弱超新星来补充我们的星等限制样本，是优化4MOST光谱样本的关键要求。然而，我们的结果是一个概念的证明，即增强也是获得最佳分类结果所必需的。
In preparation for photometric classification of transients from the Legacy Survey of Space and Time (LSST) we run tests with different training data sets. Using estimates of the depth to which the 4-m Multi-Object Spectroscopic Telescope (4MOST) Time Domain Extragalactic Survey (TiDES) can classify transients, we simulate a magnitude-limited sample reaching rAB ≈ 22.5 mag. We run our simulations with the software snmachine, a photometric classification pipeline using machine learning. The machine-learning algorithms struggle to classify supernovae when the training sample is magnitude limited, in contrast to representative training samples. Classification performance noticeably improves when we combine the magnitude-limited training sample with a simulated realistic sample of faint high-redshift supernovae observed from larger spectroscopic facilities; the algorithms’ range of average area under receiver operator characteristic curve (AUC) scores over 10 runs increases from 0.547–0.628 to 0.946–0.969 and purity of the classified sample reaches 95 per cent in all runs for two of the four algorithms. By creating new, artificial light curves using the augmentation software avocado, we achieve a purity in our classified sample of 95 per cent in all 10 runs performed for all machine-learning algorithms considered. We also reach a highest average AUC score of 0.986 with the artificial neural network algorithm. Having ‘true’ faint supernovae to complement our magnitude-limited sample is a crucial requirement in optimization of a 4MOST spectroscopic sample. However, our results are a proof of concept that augmentation is also necessary to achieve the best classification results.