Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observed $z = 2-4$ DESI LAE candidate spectra and determine their redshifts with $>$90% accuracy when validated against visually inspected redshifts. Using the $Δχ^2$ value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys.

翻译：莱曼阿尔法发射体（LAEs）作为高红移宇宙学探针，传统上依赖于窄带测光巡天进行识别。在地面光谱观测中，自动方法难以区分LAE尖锐发射峰与残余天光发射线，导致红移分类错误。我们提出一种贝叶斯光谱成分分离技术，在边缘化天光残差的同时自动确定LAE光谱红移。利用暗能量光谱巡天（DESI）观测的LAE目视检验光谱构建数据驱动先验，通过联合推断每条光谱的天光残差、LAE成分及剩余成分确定红移。在881条经DESI光谱观测的$z=2-4$ LAE候选体数据上，该方法的验证准确率超过90%（以目视检验红移为基准）。进一步，我们以管道输出的$\Delta\chi^2$值作为探测置信度代理指标，探讨中波段测光定位LAE的巡天设计选择及潜在影响。该方法实现了DESI光谱红移测定的可扩展性与准确性，其结果为未来高红移光谱巡天的LAE定位策略提供了优化建议。