This paper evaluates a signal-processing and supervised-learning pipeline for classifying SDSS DR17 astronomical spectra into stars, galaxies, and quasars. Each spectrum is represented by its measured flux and inverse-variance information, combining spectral shape with a wavelength-dependent reliability profile. After resampling onto a common logarithmic wavelength grid, the flux and inverse-variance vectors are standardized and separately compressed using principal component analysis. The resulting components are concatenated and used to train several classifiers. The best performance was obtained with the LightGBM gradient-boosting classifier, reaching $94.6\%$ accuracy and $92.1\%$ balanced accuracy on the test set.

翻译：本文评估了一种用于将SDSS DR17天文光谱分类为恒星、星系和类星体的信号处理与监督学习流水线。每条光谱由其测量通量与逆方差信息表示，结合了光谱形状与波长依赖性可靠性特征。在重采样至共同对数波长网格后，通量与逆方差向量被标准化并分别通过主成分分析进行压缩。所得分量被拼接用于训练多种分类器。最优性能由LightGBM梯度提升分类器实现，在测试集上达到$94.6\%$的准确率与$92.1\%$的平衡准确率。