本文選題：超新星候選　切入點(diǎn)：局部孤立性因子　出處：《光譜學(xué)與光譜分析》2015年01期

【摘要】：超新星是宇宙學(xué)中的"標(biāo)準(zhǔn)燭光",其在星系中爆發(fā)的概率很低,是一種特殊、稀少的天體,只有在大量觀測(cè)的星系數(shù)據(jù)中才有機(jī)會(huì)遇到,而正處于爆發(fā)期的超新星會(huì)照亮其整個(gè)星系從而在觀測(cè)獲得的星系光譜中具有較明顯的特征。但是,目前已發(fā)現(xiàn)的超新星數(shù)量相對(duì)于大量的天體而言又是非常稀少的,搜尋它們所用的計(jì)算時(shí)間成為能否進(jìn)行后續(xù)觀測(cè)的關(guān)鍵,因此需要尋找高效率的超新星搜尋方法。對(duì)超新星候選范圍進(jìn)行約減的LOF算法的時(shí)間復(fù)雜度較高,計(jì)算量大,不適用于大規(guī)模數(shù)據(jù)集。為此通過(guò)對(duì)LOF算法進(jìn)行改進(jìn),提出了一種在海量星系光譜中快速約減超新星候范圍的新方法(SKLOF)。首先對(duì)光譜數(shù)據(jù)集中離中心點(diǎn)近的數(shù)據(jù)點(diǎn)進(jìn)行數(shù)據(jù)剪枝,剪掉那些肯定不是超新星候選體的光譜數(shù)據(jù)對(duì)象,然后利用改進(jìn)的LOF算法計(jì)算剩余的光譜數(shù)據(jù)的孤立性因子并降序排列進(jìn)行離群搜索,最后獲得超新星候選體的較小的搜索范圍以便進(jìn)行后續(xù)的證認(rèn)。實(shí)驗(yàn)結(jié)果表明,該算法十分有效,不僅在精確度上有所提高,而且相比于LOF算法還進(jìn)一步縮短了算法的運(yùn)行時(shí)間,提高了算法的執(zhí)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.
【作者單位】： 遼寧科技大學(xué)理學(xué)院;中國(guó)科學(xué)院光學(xué)天文重點(diǎn)實(shí)驗(yàn)室 中國(guó)科學(xué)院國(guó)家天文臺(tái);山東大學(xué)(威海)機(jī)電與信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61202315,11078013) 遼寧省教育廳一般項(xiàng)目(L2012098)資助
【分類號(hào)】：P145.3

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本文編號(hào)：1728745