Abstract We present a deep learning framework to enhance the identification of Ly α emitters (LAEs) in the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX), an untargeted spectroscopic survey of LAEs at 1.9 5.5), and 85.1%, 78.2%, and 84.4% in the low-S/N regime. Using HETDEX LAEs independently identified by Dark Energy Spectroscopic Instrument (DESI) spectroscopy, the model recovers 99% and 93% of the high- and low-S/N LAEs, respectively. Visual attribution indicates that the CNN attends to smooth, spatially extended central emission in true positives and to irregular or noisy features in true negatives. Applied to the full HETDEX catalog, the CNN enables an S/N threshold down to 4.8 by suppressing spurious spikes across z ∼ 1.9–2.5 in the redshift distribution. Our approach facilitates HETDEX cosmological analyses by mitigating false positives in galaxy clustering and highlights the value of domain-specific deep learning for refining low-S/N spectroscopic identification in untargeted surveys.
Mukae et al. (Wed,) studied this question.