SKLOF:一种新的超新星候选范围约减算法

发布时间：2018-04-10 00:03

本文选题：超新星候选　切入点：局部孤立性因子　出处：《光谱学与光谱分析》2015年01期

【摘要】：超新星是宇宙学中的"标准烛光",其在星系中爆发的概率很低,是一种特殊、稀少的天体,只有在大量观测的星系数据中才有机会遇到,而正处于爆发期的超新星会照亮其整个星系从而在观测获得的星系光谱中具有较明显的特征。但是,目前已发现的超新星数量相对于大量的天体而言又是非常稀少的,搜寻它们所用的计算时间成为能否进行后续观测的关键,因此需要寻找高效率的超新星搜寻方法。对超新星候选范围进行约减的LOF算法的时间复杂度较高,计算量大,不适用于大规模数据集。为此通过对LOF算法进行改进,提出了一种在海量星系光谱中快速约减超新星候范围的新方法(SKLOF)。首先对光谱数据集中离中心点近的数据点进行数据剪枝,剪掉那些肯定不是超新星候选体的光谱数据对象,然后利用改进的LOF算法计算剩余的光谱数据的孤立性因子并降序排列进行离群搜索,最后获得超新星候选体的较小的搜索范围以便进行后续的证认。实验结果表明,该算法十分有效,不仅在精确度上有所提高,而且相比于LOF算法还进一步缩短了算法的运行时间,提高了算法的执行效率。
[Abstract]:Supernovae are "standard candlelight" in cosmology. They have a very low probability of exploding in galaxies. They are a special, rare object that can only be encountered in a large number of observed galactic data.The supernova in the eruption period will illuminate the whole galaxy and thus has obvious characteristics in the observed spectrum of the galaxy.However, the number of supernovae has been found to be very rare compared with a large number of celestial bodies. The computational time used to search for them is the key to further observation. Therefore, it is necessary to find a highly efficient search method for supernova.The LOF algorithm, which reduces the candidate range of supernova, has high time complexity and large amount of computation, so it is not suitable for large-scale data sets.In this paper, by improving the LOF algorithm, a new method of fast reducing the supernova range in the spectrum of massive galaxies is proposed.First of all, pruning the data points near the central point in the spectral data set, cutting off the spectral data objects that are definitely not supernova candidates.Then, the improved LOF algorithm is used to calculate the isolation factor of the remaining spectral data, and then the outlier search is carried out in descending order. Finally, the smaller search range of the supernova candidate is obtained for subsequent identification.The experimental results show that the algorithm is very effective, not only improves the accuracy, but also shortens the running time of the algorithm and improves the efficiency of the algorithm compared with the LOF algorithm.
【作者单位】：辽宁科技大学理学院;中国科学院光学天文重点实验室中国科学院国家天文台;山东大学(威海)机电与信息工程学院;
【基金】：国家自然科学基金项目(61202315,11078013) 辽宁省教育厅一般项目(L2012098)资助
【分类号】：P145.3

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