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arXiv 1201.6676astro-ph.COastro-ph.IM
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Ia型超新星光度分类的核函数PCA

Kernel PCA for type Ia supernovae photometric classification

Emille E. O. Ishida, Rafael S. de Souza

备注：
accepted for publication in MNRAS
发表信息：
MNRAS, 430, 509 (2013)
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摘要：

在这项工作中，我们提出使用核主成分分析（KPCA）结合k = 1最近邻算法（1NN）作为超新星（SNe）光度分类的框架。分类完全基于光谱确认样品内的信息，并且每次对每个新的光变曲线进行一个分类。这允许我们在新的光谱光变曲线可用的情况下更新主成分（PC）参数空间，同时还避免了为每个单独的新分类重新确定它的需要。我们将该方法应用于\textit{Supernova Photometric Classification Challenge}（SNPCC）数据集的不同实例。当信噪比$\geq$为5时，我们的方法在所有分析的数据样本中提供了良好的纯度结果。因此，我们可以声明，如果作为SNPCC后的样品今天可用，我们将能够对纯度$\gtrsim$ 90%（D $_{7}$ +SNR3）的初始数据集的$\approx 15%$进行分类。从原来的SNPCC样品的结果，报告为红移的函数，表明我们的方法提供了高纯度（高达$\approx 97%$），特别是在$0.2\leq z < 0.4$的范围内，当从SNPCC的结果相比，同时保持一个中等的品质因数（$\approx 0.25$）。我们还提出了SNe光度分类的结果，仅使用前最大时期，获得63%的纯度和77%的成功分类率（SNR $\geq$ 5）。结果对每个光变曲线中包含的信息敏感，因此，更高质量的数据点导致更高的成功分类率。只要提供训练样本和测试样本，该方法就可以灵活地应用于其他天体物理瞬变。

英文摘要：

In this work, we propose the use of Kernel Principal Component Analysis (KPCA) combined with k = 1 nearest neighbour algorithm (1NN) as a framework for supernovae (SNe) photometric classification. The classification is entirely based on information within the spectroscopic confirmed sample and each new light curve is classified one at a time. This allows us to update the principal component (PC) parameter space if a new spectroscopic light curve is available while also avoids the need of re-determining it for each individual new classification. We applied the method to different instances of the \textit{Supernova Photometric Classification Challenge} (SNPCC) data set. Our method provide good purity results in all data sample analysed, when SNR$\geq$5. As a consequence, we can state that if a sample as the post-SNPCC was available today, we would be able to classify $\approx 15%$ of the initial data set with purity $\gtrsim$ 90% (D$_{7}$+SNR3). Results from the original SNPCC sample, reported as a function of redshift, show that our method provides high purity (up to $\approx 97%$), specially in the range of $0.2\leq z < 0.4$, when compared to results from the SNPCC, while maintaining a moderate figure of merit ($\approx 0.25$). We also present results for SNe photometric classification using only pre-maximum epochs, obtaining 63% purity and 77% successful classification rates (SNR$\geq$5). Results are sensitive to the information contained in each light curve, as a consequence, higher quality data points lead to higher successful classification rates. The method is flexible enough to be applied to other astrophysical transients, as long as a training and a test sample are provided.

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