Rare Object Search From Low-S/N Stellar Spectra in SDSS
Rare Object Search From Low-S/N Stellar Spectra in SDSS
复制标题
从 SDSS 中的低信噪比恒星光谱中搜索稀有天体
DOI：
10.1109/access.2020.2983745
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Wei Peng
中科院分区：
文献类型：
--
作者：
Wu Minglei;Pan Jingchang;Yi Zhenping;Wei Peng
白矮星+主序星（WDMS）和灾变变星（CV）等稀有天体对于研究星系和宇宙的演化非常重要。斯隆数字巡天（SDSS）等大型巡天活动获得的大量光谱是这些稀有天体的丰富来源。然而，这些光谱中有相当一部分是低信噪比光谱。这些低信噪比光谱包含与高信噪比光谱类似的有用信息，更好地利用这些光谱可以显着提高发现稀有物体的机会。然而，对它们的研究却很少。在本研究中，我们提出了一种基于 PCA（主成分分析）和 CFSFDP（通过快速搜索和查找密度峰进行聚类）相结合的新方法，从低信噪比光谱中搜索稀有物体。 PCA首先从高S/N光谱中提取主成分以生成一般特征光谱，并用这些一般特征光谱重建低S/N恒星光谱。然后CFSFDP计算重构光谱的局部密度$\rho$和距离$\delta$，并通过决策图快速准确地选择异常值。我们首先将我们的方法应用于SDSS恒星分类模板库中的光谱，并添加白高斯噪声来搜索稀有物体（碳星、碳白矮星、碳线、白矮星和磁性白矮星）。然后我们将我们的方法应用于来自 SDSS 的不同低信噪比的观测光谱，并与 Lick-index+K-means 和支持向量机 (SVM) 进行比较。实验结果表明，与其他方法相比，我们的方法具有更高的效率。
Rare objects such as white dwarf+main sequence (WDMS) and cataclysmic variables (CVs) are very important for studying the evolution of the galaxy and the universe. The large amount of spectra obtained by the large sky surveys such as the Sloan Digital Sky Survey (SDSS) are rich sources of these rare objects. However, a considerable fraction of these spectra are low-S/N spectra. These low-S/N spectra contain similar useful information as the high-S/N spectra, and making better use of these spectra can significantly improve the chance of finding rare objects. Nevertheless, little research has been done on them. In this study we propose a novel method based on the combination of PCA (Principal Components Analysis) and CFSFDP (Clustering by Fast Search and Find of Density Peak) to search for rare objects from low-S/N spectra. The PCA first extracts principal components from high-S/N spectra to generate general feature spectra and reconstructs low-S/N stellar spectra with these general feature spectra. Then the CFSFDP calculates the Local Density $\rho $ and the Distance $\delta $ of the reconstructed spectra, and select the outliers through the decision graph quickly and accurately. We first apply our method to spectra in SDSS stellar classification template library with adding white gaussian noise to search for rare objects (carbon stars, carbon white dwarfs, carbon_lines, white dwarfs and white dwarfs magnetic). Then we apply our method to observed spectra with different low-S/Ns from SDSS and compared with Lick-index+K-means and Support Vector Machines (SVM). The experimental results show that our method has a higher efficiency compared to other methods.
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影响因子：
6.5
作者：
B. Arsioli;Y-L. Chang
通讯作者：
B. Arsioli;Y-L. Chang
DOI：
10.1111/j.1365-2966.2012.22012.x
发表时间：
2012-09
影响因子：
4.8
作者：
J. Simpson;P. Cottrell;C. Worley
通讯作者：
J. Simpson;P. Cottrell;C. Worley
DOI：
--
发表时间：
2012-02
期刊：
Guang pu xue yu guang pu fen xi = Guang pu
影响因子：
--
作者：
Bin Jiang;A. Luo;Yongheng Zhao
通讯作者：
Bin Jiang;A. Luo;Yongheng Zhao
影响因子：
4.8
作者：
Mark Kennedy;Mark Kennedy;P. Callanan;P. Garnavich;M. Fausnaugh;J. C. Zinn
通讯作者：
Mark Kennedy;Mark Kennedy;P. Callanan;P. Garnavich;M. Fausnaugh;J. C. Zinn
影响因子：
4.8
作者：
F. Jiménez-Esteban;F. Jiménez-Esteban;S. Torres;A. Rebassa-Mansergas;G. Skorobogatov;E. Solano;C. C
通讯作者：
F. Jiménez-Esteban;F. Jiménez-Esteban;S. Torres;A. Rebassa-Mansergas;G. Skorobogatov;E. Solano;C. C