基于W几何形的恒星光谱分子带检测

Spectral Molecular Band Detection Based on W Geometry

研究关注由分子内部能级变化引起的光谱分子带的识别和检测,有助于研究恒星光谱类型和参数估计。首先从分子带的曲线趋势出发,运用曲线分析对分子带进行识别,并剔除具备W形但下降趋势明显的伪分子带。借鉴多类型多分类准则的识别思路,将检测出的分子带尖峰深度、W形宽度、曲线趋势和回升趋势四个参数作为训练特征。这四个参数综合考虑了始点变化速率、曲线变化趋势、极值点分布和曲线形状因素。其次,为了验证该方法的可行性与可靠性,利用LightGBM(light gradient boosting machine)模型分别对F型恒星光谱和分子带特征参数进行识别,准确率分别为97.62%和99.16%,进一步验证了所提取分子带的准确性。本工作不仅能挖掘出晚期恒星,提高数据标签的准确性,还能在准确识别的基础上,利用LightGBM机器学习模型检测未知型光谱自动识别晚期恒星,提高了识别效率并且减少了内存占用。

This study focuses on identifying and detecting spectral molecular bands caused by changes in internal energy levels of molecules,which contributes to the research of stellar spectral types and parameter estimation.First,considering the curve trend of molecular bands,pseudo molecular bands that have a W shape but an obvious downward trend should be eliminated by using curve analysis to identify molecular bands.Bringing the identification idea of multi-type and multi-classification criteriainto the model,the four parameters of the detected molecular band peak depth,W-shaped width,curve trend,and rebound trend are adoptedas training features,which consider comprehensivelythe change rate of starting point,change trend of curve,extreme point distribution and the factors of curve shape.Secondly,the LightGBM(Light Gradient Boosting Machine)model is used to identify the spectral and molecular band characteristic parameters of F-type stars with an accuracy of 97.62%and 99.16%,respectively,to verify the feasibility and reliability of this method.This work can not only excavate the late stars and improve the accuracy of data labels but also automatically identify the late stars by using the LightGBM machine learning model to detect the unknown spectrum based on accurate recognition,which improves recognition efficiency and reduces memory occupation.