Rubin-LSST

Abstract:

Radio wavelengths offer the unique possibility of tracing the total star-formation rate in galaxies, both obscured and unobscured. As such, they may provide the most robust measurement of the star-formation history of the Universe. In this chapter we highlight the constraints that the SKA can place on the evolution of the star-formation history of the Universe, the survey area required to overcome sample variance, the spatial resolution requirements, along with the multi-wavelength ancillary data that will play a major role in maximising the scientific promise of the SKA. The required combination of depth and resolution means that a survey to trace the star formation in the Universe should be carried out with a facility that has a resolution of at least ~0.5arcsec, with high sensitivity at < 1 GHz. We also suggest a strategy that will enable new parameter space to be explored as the SKA expands over the coming decade.

Abstract:

Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is presented. Simulated and real data from SDSS DR12 were used to train and test the proposed models. We show that approaches which include careful data preparation and model design offer a significant improvement in comparison with several competing machine learning algorithms. Standard implementations of most regression algorithms have as the objective the minimization of the sum of squared errors. For redshift inference, however, this induces a bias in the posterior mean of the output distribution, which can be problematic. In this paper we directly target minimizing $\Delta z = (z_\textrm{s} - z_\textrm{p})/(1+z_\textrm{s})$ and address the bias problem via a distribution-based weighting scheme, incorporated as part of the optimization objective. The results are compared with other machine learning algorithms in the field such as Artificial Neural Networks (ANN), Gaussian Processes (GPs) and sparse GPs. The proposed framework reaches a mean absolute $\Delta z = 0.0026(1+z_\textrm{s})$, over the redshift range of $0 \le z_\textrm{s} \le 2$ on the simulated data, and $\Delta z = 0.0178(1+z_\textrm{s})$ over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNz used in the literature. We also investigate how the relative size of the training set affects the photometric redshift accuracy. We find that a training set of \textgreater 30 per cent of total sample size, provides little additional constraint on the photometric redshifts, and note that our GP formalism strongly outperforms ANNz in the sparse data regime for the simulated data set.