Sky background subtraction is a critical step in Multi-objective Fiber spectra process. However, current subtraction relies mainly on sky fiber spectra to build Super Sky. These average spectra are lacking in the modeling of the environment surrounding the objects. To address this issue, a sky background estimation model: Sky background building based on Mutual Information (SMI) is proposed. SMI based on mutual information and incremental training approach. It utilizes spectra from all fibers in the plate to estimate the sky background. SMI contains two main networks, the first network applies a wavelength calibration module to extract sky features from spectra, and can effectively solve the feature shift problem according to the corresponding emission position. The second network employs an incremental training approach to maximize mutual information between representations of different spectra to capturing the common component. Then, it minimizes the mutual information between adjoining spectra representations to obtain individual components. This network yields an individual sky background at each location of the object. To verify the effectiveness of the method in this paper, we conducted experiments on the spectra of LAMOST. Results show that SMI can obtain a better object sky background during the observation, especially in the blue end.

翻译：天空背景扣除是多目标光纤光谱处理中的关键步骤。然而，当前扣除方法主要依赖天空光纤光谱构建超天光光谱。这些平均光谱缺乏对目标周围环境的建模。为解决此问题，本文提出一种天空背景估计模型：基于互信息网络的天空背景建模（SMI）。SMI基于互信息与增量训练方法，利用观测板中所有光纤的光谱来估计天空背景。SMI包含两个主要网络：第一个网络采用波长校准模块从光谱中提取天空特征，并能根据对应的发射位置有效解决特征偏移问题；第二个网络采用增量训练方法，通过最大化不同光谱表征间的互信息以捕获公共成分，继而最小化相邻光谱表征间的互信息以获得独立成分。该网络可在每个目标位置生成独立的天空背景。为验证本文方法的有效性，我们在LAMOST光谱数据上进行了实验。结果表明，SMI能够在观测过程中获得更优的目标天空背景，尤其在蓝端区域表现突出。