A CNN--Transformer Denoiser for low-$S/N$ Galaxy Spectra: Stellar Population Recovery in Synthetic Tests

Abstract: Stellar population measurements in integral field unit surveys are often limited by low signal-to-noise ratios (S/N) in low-surface-brightness spaxels. Using controlled synthetic experiments, we test whether deep-learning-based denoising can recover stellar population information without spatial binning. We introduce the Enhanced U-Net Transformer (EUT), a one-dimensional CNN-Transformer model trained on 90,000 synthetic spectra constructed from MILES simple stellar population models following Lee et al. (2023). Wavelength-dependent noise is injected on the fly to emulate SAMI-like data with S/N = 5-20, measured in a 4484.77-4573.12 Angstrom continuum window. On an independent test set of 10,000 spectra, EUT reduces the full-spectrum RMS residual by about 96.5 percent at S/N = 5 and about 94 percent at S/N = 20, with recovery rates of at least 99.8 percent. In fixed windows around Ca II H, Hdelta, Hbeta, Fe I 4383, Mg b, and Na D, residuals decrease by more than about 88 percent while preserving line-profile structure. In downstream pPXF fitting, the RMS scatter in recovered mass-weighted age decreases from about 0.41 to 0.25 dex at S/N = 5 and from about 0.32 to 0.22 dex at S/N = 10. For mass-weighted metallicity, [M/H], the scatter decreases from about 0.45 to 0.36 dex and from about 0.32 to 0.28 dex, respectively. At S/N = 20, denoised and noisy inputs give consistent results within the synthetic-test uncertainties. These experiments suggest that hybrid CNN-Transformer denoisers can improve low-S/N spectra for stellar population studies, although validation with observed spectra is still required.