中文摘要：

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。

英文摘要：

Supernova （SN） is called the “standard candles” in the cosmology ,the probability of outbreak in the galaxy is very low and is a kind of special ,rare astronomical objects .Only in a large number of galaxies ,we have a chance to find the superno-va .The supernova which is in the midst of explosion will illuminate the entire galaxy ,so the spectra of galaxies we obtained have obvious features of supernova .But the number of supernova have been found is very small relative to the large number of astro-nomical objects .The time computation that search the supernova be the key to weather the follow-up observations ,therefore it needs to look for an efficient method .The time complexity of the density-based outlier detecting algorithm （LOF） is not ideal , which effects its application in large datasets .Through the improvement of LOF algorithm ,a new algorithm that reduces the searching range of supernova candidates in a flood of spectra of galaxies is introduced and named SKLOF .Firstly ,the spectra datasets are pruned and we can get rid of most objects are impossible to be the outliers .Secondly ,we use the improved LOF al-gorithm to calculate the local outlier factors （LOF） of the spectra datasets remained and all LOFs are arranged in descending or-der .Finally ,we can get the smaller searching range of the supernova candidates for the subsequent identification .The experi-mental results show that the algorithm is very effective ,not only improved in accuracy ,but also reduce the operation time com-pared with LOF algorithm with the guarantee of the accuracy of detection .