一维光谱恢复与海量光谱红移自动测量方法研究

At present, there are some distortions in LAMOST 1D-spectra, including lower signal-to-noise ratio, flux-distortions of continuum and spectral lines, and some data missing. The above distortions result in poor effect of the existing measurement algorithms for massive spectra, and then affect the smooth realization of the scientific goal of LAMOST. Based on the signal characteristic and physical meanings of the LAMOST spectra, in this item, we will study the follows: (1) LAMOST 1D-spectra restoration methods making a restored spectrum approximate to the true one, which consist of designing the noise suppression methods, building the describing function of effective curves and estimating the parameters, establishing evaluation standard of the LAMOST 1D-spectra. The key scientific problem is finding out the distinguishing features between the signals and noise. (2) Automatic measurements of the galaxy redshifts based on the spectral lines matching methods, which consist of designing the measurements method of higher redshifts, designing the measurements method of redshifts in lower signal-to-noise ratio, and measuring redshifts by processing the spectral blue band and red band separately. The key scientific problem is choosing, automatically detecting and recognizing the feature spectral lines. The item belongs to the interdisciplinary of astronomy, pattern recognition and signal and information processing. The research results of the item can not only meet the requirements of LAMOST, but also provide reference algorithm to other similar sky survey item, detection and processing of weaker signal, or reduction of complex signals.

目前的LAMOST一维光谱存在信噪比偏低、连续谱与谱线流量失真、光谱中部分数据缺失等问题，导致已有的海量光谱自动测量算法效果差，影响LAMOST既定科学目标的顺利实现。基于LAMOST光谱的信号特点与物理意义，项目研究：（1）LAMOST一维光谱恢复技术，实现恢复光谱逼近真实值。包括设计噪声抑制方法；建立仪器效率曲线描述函数并估计参数；建立LAMOST一维光谱质量评价标准。其中，寻找光谱信号与噪声的区分特征是关键科学问题。（2）采用谱线匹配方法，准确快速地自动测量星系光谱红移。包括设计高红移测量方法、较低信噪比下红移测量方法、对光谱蓝、红波段分别处理来测量红移的方法。其中，特征谱线的选取、自动检测与识别是关键科学问题。项目属于天文学、模式识别和信号与信息处理的交叉学科研究，研究成果不仅满足LAMOST的需求，也可以为类似的光谱巡天项目、弱信号检测与处理及复杂信号处理提供算法参考。

目前的LAMOST一维光谱存在信噪比偏低、连续谱与谱线流量失真、光谱中部分数据缺失等问题，导致已有的海量光谱自动测量算法效果差，影响LAMOST既定科学目标的顺利实现。基于LAMOST光谱的信号特点与物理意义，项目开展了LAMOST一维光谱恢复、海量星系光谱红移自动测量研究。设计了4种适合LAMOST的噪声抑制算法，相比于对含噪声光谱直接测量红移，光谱经降噪后，红移测量值准确率提高较多。此外，算法还应用于从信噪比偏低的天光光谱中检测相对较弱的[SII]双线，并配合人工检查，对银河系HII区证认。项目研究分析影响LAMOST一维光谱质量的因素，设计了对这些因素的检测和量化算法及LAMOST一维光谱质量评估软件。设计了基于模板匹配的高红移测量算法。项目基于谱线特征，对LAMOST星系光谱进行了更细致的分类。项目研究成果可以为LAMOST河外巡天观测和数据处理，以及类似的光谱巡天项目提供算法参考。同时，项目执行期间，扩大了天文相关学科在地方的影响力，带动了德州学院天文学科建设和研究队伍培养，促进了地方高校的人员利用我国的大科学装置，开展天文研究。