Machine learning synthetic spectra for probabilistic redshift
  estimation: SYTH-Z
  Photometric redshift estimation algorithms are often based on representative
data from observational campaigns. Data-driven methods of this type are subject
to a number of potential deficiencies, such as sample bias and incompleteness.
Motivated by these considerations, we propose using physically motivated
synthetic spectral energy distributions in redshift estimation. In addition,
the synthetic data would have to span a domain in colour-redshift space
concordant with that of the targeted observational surveys. With a matched
distribution and realistically modelled synthetic data in hand, a suitable
regression algorithm can be appropriately trained; we use a mixture density
network for this purpose. We also perform a zero-point re-calibration to reduce
the systematic differences between noise-free synthetic data and the
(unavoidably) noisy observational data sets. This new redshift estimation
framework, SYTH-Z, demonstrates superior accuracy over a wide range of
redshifts compared to baseline models trained on observational data alone.
Approaches using realistic synthetic data sets can therefore greatly mitigate
the reliance on expensive spectroscopic follow-up for the next generation of
photometric surveys.
