Tuning target selection algorithms to improve galaxy redshift estimates
  We showcase machine learning (ML) inspired target selection algorithms to
determine which of all potential targets should be selected first for
spectroscopic follow up. Efficient target selection can improve the ML redshift
uncertainties as calculated on an independent sample, while requiring less
targets to be observed. We compare the ML targeting algorithms with the Sloan
Digital Sky Survey (SDSS) target order, and with a random targeting algorithm.
The ML inspired algorithms are constructed iteratively by estimating which of
the remaining target galaxies will be most difficult for the machine learning
methods to accurately estimate redshifts using the previously observed data.
This is performed by predicting the expected redshift error and redshift offset
(or bias) of all of the remaining target galaxies. We find that the predicted
values of bias and error are accurate to better than 10-30% of the true values,
even with only limited training sample sizes. We construct a hypothetical
follow-up survey and find that some of the ML targeting algorithms are able to
obtain the same redshift predictive power with 2-3 times less observing time,
as compared to that of the SDSS, or random, target selection algorithms. The
reduction in the required follow up resources could allow for a change to the
follow-up strategy, for example by obtaining deeper spectroscopy, which could
improve ML redshift estimates for deeper test data.
