The DESI survey will collect data on a large number of galaxies. We shall present our work on techniques to maximise the physical data retrieval for the purpose of galaxy evolution analysis. We focus on the use of machine learning algorithms as a means of intelligent noise reduction, primarily auto encoder methods. In addition, we make use of the latent space representations produced by such methods to automate the selection of similar spectra for stacking.

In anticipation of the DESI SV data we first train our models using SDSS spectra with high signal to noise, adding artificial noise to create a training set. Once the SV data are available we shall look into both training the model on the data, and also at transfer learning between the two datasets.