Spender establishes a restframe for galaxy spectra that has higher resolution and larger wavelength range than the spectra from which it is trained. The model can be trained from spectra at different redshifts or even from different instruments without the need to standardize the observations. Spender also has an explicit, differentiable redshift dependence, which can be coupled with a redshift estimator for a fully data-driven spectrum analysis pipeline. The code describes the restframe spectrum by an autoencoder and transforms the restframe model to the observed redshift; it also matches the spectral resolution and line spread function of the instrument.

AESTRA (Auto-Encoding STellar Radial-velocity and Activity) uses deep learning for precise radial velocity measurements in the presence of stellar activity noise. The architecture combines a convolutional radial-velocity estimator and a spectrum auto-encoder. The input consists of a collection of hundreds or more of spectra of a single star, which span a variety of activity states and orbital motion phases of any potential planets. AESTRA does not require any prior knowledge about the star.