多目标光纤光谱望远镜可以在一次观测中获得大量的不同天体的光谱数据。 从天体探测到的光在通过光纤之后, 再通过光谱仪狭缝, 然后在CCD传感器中成像为二维光谱图; 之后经过光纤光谱数据处理系统的一系列软件处理, 最终输出可供天文界使用的一维光谱并存储起来。 一维光谱是天文学家研究目标天体的主要手段, 它是通过处理二维光谱图得到的。 以LAMOST为例, 望远镜系统在一次观测后首先会得到32幅由250条光纤光谱组成的二维光谱, 然后经过一系列的处理得到一维光谱。 在这个过程中, 会有很多因素影响到最终一维光谱的精确度。 比如由于望远镜使用时间的增加, 某些元件会产生磨损、 老化或变形, 使得二维光谱中光纤形状会产生一定程度的弯曲, 这种弯曲在二维光谱的两侧表现得尤为明显。 在一幅常见的二维光谱中, 纵坐标方向代表了抽取的一维光谱的波长方向, 横坐标方向代表了抽取的一维光谱的流量方向, 这种弯曲形变的产生会影响到之后的波长定标和流量定标, 使得抽取的一维谱信息不准确。 目前初步的解决办法是通过与定标灯谱的比对来尽量减少其影响。 但这样不仅造成了时间和人力的浪费, 而且准确率和效率不高。 就这一现状, 提出了一种基于曲线距离法的思想, 将弯曲的二维谱线校直: 首先采用灰度重心法将一幅二维光谱中的250条光纤中心轨迹进行定位, 将异常点采用稳健的局部回归方法剔除; 然后将中心轨迹进行曲线拟合, 得到光纤中心轨迹的方程; 通过模仿曲线变弯的逆过程, 即保持轨迹上两点间的曲线距离不变, 再将弯曲的光谱映射到竖直的法线上, 完成校直过程。 在整个过程中保持各个对应点的灰度值不变, 通过边缘处理和插值运算解决产生的像素点稀疏问题。 最后采用累加法进行一维谱抽取, 并将校直后抽取的一维光谱与未校直抽取的一维光谱进行比对, 比对后可发现校直前后在一维光谱的两端差别较大, 其差值谱线也说明了这一点。 该方法实现了二维光谱的自动校直, 大大提高了抽取一维谱的效率和准确性。 二维光谱的预处理和校直方法首先在LAMOST数据上进行验证, 鉴于多目标光纤光谱望远镜系统原理的相似性, 该处理方法也适用于其他的多目标光纤光谱望远镜系统, 具有较好的参考和应用价值。

The multi-target fiber spectroscopic telescope can obtain a large number of spectral data of different celestial bodies in one observation. The light detected from the celestial body passes through the slit of the spectrometer, and after passing through the optical fiber, it is transmitted to the CCD sensor to obtain a two-dimensional spectral image. After a series of processing by the fiber optic spectral data processing system, the available spectral data is finally output and stored. The one-dimensional spectrum is the main means by which we obtain information about the target celestial body. The LAMOST telescope is used to obtain the observed celestial information. Taking LAMOST as an example, before obtaining a one-dimensional spectrum, the telescope system first obtains a two-dimensional spectrum consisting of 250 optical fiber spectra after one observation, and then undergoes a series of processing to obtain a one-dimensional spectrum. However, due to the increased use time of the telescope, the components will wear and age, which will cause a certain degree of bending of the fiber trajectory in the two-dimensional spectrum. This bending is particularly evident on both sides of the two-dimensional spectrum. The ordinate direction of a two-dimensional spectrum represents the wavelength direction of the extracted one-dimensional spectrum, and the abscissa direction represents the flow direction of the extracted one-dimensional spectrum. The generation of such deformation affects the subsequent wavelength calibration and flow. The calibration makes the extracted one-dimensional spectrum information inaccurate. The current initial solution is to minimize the impact by comparing with the calibration lamp spectrum. This not only causes a waste of time and manpower, but also has low accuracy and efficiency. In this paper, we propose a method of straightening the curved two-dimensional line based on the curve distance method. Firstly, the gray center of gravity method is used to locate the 250 fiber center trajectories in a two-dimensional spectrum, and the abnormal point is set. The robust local regression method is used to eliminate the curve, and then the center trajectory is curve-fitted to obtain the equation of the fiber trajectory. By simulating the inverse process of the curve bending, that is, keeping the curve distance between the two points on the trajectory unchanged, and then bending the spectrum Map to the vertical normal line to complete the straightening process. At the same time, the gray value of each corresponding point is kept unchanged, and the sparse problem of generating pixel points is solved by edge processing and interpolation operation. Finally, the one-dimensional spectrum extraction is performed by the accumulation method, and the one-dimensional spectrum extracted after straightening is compared with the undimensionally extracted one-dimensional spectrum. The difference between the two ends of the one-dimensional spectrum before and after the straightening is large, and the difference is passed. The value line also illustrates this. The method realizes the automatic alignment of the two-dimensional spectrum, which greatly improves the efficiency and accuracy of extracting the one-dimensional spectrum. The two-dimensional spectral pre-processing and alignment method proposed in this paper is validated on the LAMOST data at first. Considering the similarity of the principle of the multi-target Optical Fiber Spectral Telescope system, this method can also be applied to other multi-target Optical Fiber Spectral Telescope systems, and has good reference and application value.