从海量星系光谱中搜索超新星候选光谱

发布时间：2018-04-02 16:08

本文选题：超新星　切入点：光谱分解　出处：《中国科学:物理学力学天文学》2010年10期

【摘要】：完善了一种在海量星系光谱中快速搜索超新星光谱的方法.该方法主要包括五个步骤,第一,光谱的预处理,包括去噪、退红移等处理过程;第二,对每一条光谱进行星系成分和超新星成分的快速分解,并计算本文定义的超新星统计特征描述向量;第三,样本约减,即利用基于局部孤立性因子的离群搜索算法对海量光谱数据样本进行约减;第四,对约减后的结果与所有模板进行交叉相关匹配,根据匹配结果及相应参数获得初步的超新星候选光谱;第五,人工排除,即根据超新星光谱明显的谱线特征排除一些误匹配结果,最终得到特征相对明显的超新星候选光谱.根据上述方法,对选自美国斯隆数字巡天第七期释放数据(SDSS-DR7)的具有较高信噪比的294843条星系光谱进行搜索,获得了36条含爆发超新星的星系光谱侯选,其中9个为本文首次发现并报告,15个已以快报形式被本文作者公开报告,另外12个为已发现并公开命名的超新星光谱.根据最匹配模板的类型,24个未命名的超新星候选光谱包括20个Ia型,3个Ic型和1个II型样本.
[Abstract]:A method of fast searching supernova spectrum in the spectrum of massive galaxies is developed.The method consists of five steps: first, spectral pretreatment, including denoising, redshift, etc.; second, fast decomposition of each spectrum by galaxy and supernova components.And calculate the supernova statistical feature description vector defined in this paper. Third, sample reduction, that is, using outlier search algorithm based on local isolation factor to reduce massive spectral data samples; fourth,The reduced results were cross-correlated with all the templates, and the initial supernova candidate spectra were obtained according to the matching results and the corresponding parameters.According to the obvious spectral line characteristics of the supernova spectrum, some mismatch results are excluded, and finally, the candidate spectra with relatively obvious characteristics are obtained.According to the above method, the spectrum of 294843 galaxies with high signal-to-noise ratio (SNR) selected from the Sloan Digital Survey (SDSS-DR7) was searched, and 36 galaxies containing burst supernovae were obtained.Nine of them were first discovered and reported in this paper, 15 have been reported publicly by the authors in the form of KuaiBao, and the other 12 have been discovered and publicly named supernova spectra.According to the type of the most matched template, 24 unnamed supernova candidate spectra include 20 Ia types, 3 IC types and 1 type II sample.
【作者单位】：中国科学院自动化研究所;辽宁科技大学理学院;中国科学院光学天文重点实验室(国家天文台);
【基金】：国家自然科学基金资助项目(批准号:60773040,10973021)
【分类号】：P145.3

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