Emergent Denoising of SDSS Galaxy Spectra Through Unsupervised Deep Learning

Abstract

Spectroscopy represents the ideal observational method to maximally extract information from galaxies regarding their star formation and chemical enrichment histories. However, absorption spectra of galaxies prove rather challenging at high redshift or in low mass galaxies, due to the need to spread the photons into a relatively large set of spectral bins. For this reason, the data from many state-of-the-art spectroscopic surveys suffer from low signal-to-noise (S/N) ratios, and prevent accurate estimates of the stellar population parameters. In this paper, we tackle the issue of denoising an ensemble by the use of unsupervised Deep Learning techniques trained on a homogeneous sample of spectra over a wide range of S/N. These methods reconstruct spectra at a higher S/N and allow us to investigate the potential for Deep Learning to faithfully reproduce spectra from incomplete data. Our methodology is tested on three key absorption line strengths and is compared with (noiseless) fitted data to assess retrieval biases. The results suggest a standard Autoencoder as a very powerful method that does not introduce systematics in the reconstruction. We also emphasise the need for careful analysis, demonstrating that classical signal-processing methods like Butterworth filters can yield spectra that appear smoothed yet deviate significantly from the true, underlying signals. Denoising methods with minimal bias will maximise the scientific return of ongoing and future spectral surveys such as DESI, WEAVE, or WAVES.