Abstract

Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally targeted with specialized narrow-band photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines, 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 DESI (Dark Energy Spectroscopic Instrument) LAE targets 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 910 photometrically targeted z = 2-4 DESI LAE candidate spectra and determine their redshifts with >90% accuracy compared to visually inspected redshifts. Using the chi-squared 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 spectroscopic redshifts in DESI and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys, such as DESI -2.