A Convolutional Neural Network-Transformer Denoiser for Low-signal-to-noise-ratio 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/Ns) in low-surface-brightness spaxels. Using controlled synthetic experiments, we investigate whether a deeplearning-based denoising can recover stellar population information from such spectra without requiring spatial binning. We introduce the Enhanced U-Net Transformer (EUT), a one-dimensional convolutional neural network-transformer model trained on 90,000 synthetic spectra constructed from MILES simple stellar population (SSP) models following J. H. Lee et al., with wavelength-dependent noise injected on the fly to emulate SAMI-like data (S/N - 5-20, measured in a 4484.77-4573.12 A continuum window). Utilizing an independent test set of 10,000 spectra, the EUT reduces the full-spectrum rms residual by -96.5% at S/N = 5(and by -94% at S/N = 20), achieving recovery rates of -99.8% (the Pearson correlation coefficient between the noise-free and comparison spectra expressed in percent). In fixed windows around Ca II H, Hδ, Hβ, Fe I 4383, Mg b, and Na D, residuals decrease by -88% while preserving line-profile structure. In downstream analysis withPPXF we assess parameter recovery using the Pearson correlation coefficient Rp and the rms scatter: the scatter in recovered mass-weighted age decreases from -0.41 to -0.25 dex at S/N = 5 and from -0.32 to -0.22 dex at S/N = 10; the corresponding mass-weighted global metallicity, [M/H], scatter decreases from -0.45 to-0.36 dex and from -0.32 to -0.28 dex. At S/N = 20, denoising yields results consistent with those from the noisy inputs within the synthetic-test uncertainties. These controlled experiments suggest that hybrid CNN-transformer denoisers can enhance the usable low-surface-brightness area for stellar population studies, although further validation with observed spectra will be needed before practical application.