Automatic purification of skylight spectrum based on an autoencoder

Abstract

Abstract In the realm of astronomical spectroscopic observation, the purity of skylight spectra is crucial for accurate analysis, often complicated by interference from neighboring celestial objects. Addressing this challenge, a novel automatic purification algorithm is proposed in this study, leveraging the power of auto-coding mechanisms. By harnessing the inherent data features and shared representations between skylight and target objects, this method effectively isolates skylight spectra from residual signals of neighboring objects. Central to this approach is the utilization of an encoder-decoder framework. The encoder dynamically learns the combined features of skylight and target objects, generating respective coding vectors. These vectors are then utilized to subtract the target-specific features from the skylight coding space, facilitating the extraction of pure skylight characteristics. Subsequently, the decoder reconstructs the refined skylight data, effectively eliminating residual components associated with neighboring celestial bodies. A notable strength of this algorithm lies in its ability to perform feature extraction and purification autonomously, without relying on labeled data. Experimental validation conducted on LAMOST-DR5 datasets demonstrates the efficacy of the proposed method. By effectively removing spectral information from target objects within skylight spectra, the algorithm yields comparatively pure skylight spectra, paving the way for enhanced astronomical spectroscopic analyses.