Status and accuracy of the Monte Carlo generators for luminosity measurements

The status and accuracy of the precision Monte Carlo generators used for luminosity measurements at flavour factories is reviewed.It is shown that,thanks to a considerable,long-term effort in tuned comparisons between the predictions of independent programs,as well as in the validation of the generators against the presently available calculations of the next-to-next-to-leading order QED corrections to Bhabha scattering,the theoretical accuracy reached by the most precise tools is of about one per mille.This error estimate is valid for realistic experimental cuts,appears to be quite robust and is already sufficient for very accurate luminosity measurements.However,recent progress and possible advances to further improve it are also discussed.     

Status and accuracy of the Monte Carlo generators for luminosity measurements

The status and accuracy of the precision Monte Carlo generators used for luminosity measurements at flavour factories is reviewed. It is shown that, thanks to a considerable, long-term effort in tuned comparisons between the predictions of independent programs, as well as in the validation of the generators against the presently available calculations of the next-to-next-to-leading order QED corrections to Bhabha scattering, the theoretical accuracy reached by the most precise tools is of about one per mille. This error estimate is valid for realistic experimental cuts, appears to be quite robust and is already sufficient for very accurate luminosity measurements. However, recent progress and possible advances to further improve it are also discussed.     

基于非参数回归与Adaboost的恒星光谱自动分类方法

通过对恒星光谱进行分析可以研究银河系的演化与结构等科学问题,光谱分类是恒星光谱分析的基本任务之一。提出了一种结合非参数回归与Adaboost对恒星光谱进行MK分类的方法,将恒星按光谱型和光度型进行分类,并识别其光谱型的次型。恒星光谱的光谱型及其次型代表了恒星的表面有效温度,而光度型则代表了恒星的发光强度。在同一种光谱型下,光度型反映了谱线形状细节的变化,因此光度型的分类必须在光谱型分类基础上进行。本文把光谱型的分类问题转化为对类别的回归问题,采用非参数回归方法进行恒星光谱型和光谱次型的分类;基于Adaboost方法组合一组K近邻分类器进行光度型分类,Adaboost将一组弱分类器加权组合产生一个强分类器,提升光度型的识别率。实验验证了所提出分类方法的有效性,光谱次型识别的精度达到0.22,光度型的分类正确率达到84%以上。实验还对比了两种KNN方法与Adaboost方法的光度型分类,结果表明,利用KNN方法对光度型分类精度低,而基于弱分类器KNN的Adaboost方法将识别率大幅提升。