Data-driven, interpretable photometric redshifts trained on heterogeneous and unrepresentative data

Understand

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift.

• This conceptually novel approach combines the advantages of both machine-learning and template-fitting methods by building template SEDs directly from the training data.
• This is made computationally tractable with Gaussian Processes operating in flux--redshift space, encoding the physics of redshift and the projection of galaxy SEDs onto photometric band passes.
• This method alleviates the need of acquiring representative training data or constructing detailed galaxy SED models; it requires only that the photometric band passes and calibrations be known or have parameterized unknowns.