A re-assessment of strong line metallicity conversions in the machine
  learning era
  Strong line metallicity calibrations are widely used to determine the gas
phase metallicities of individual HII regions and entire galaxies. Over a
decade ago, based on the Sloan Digital Sky Survey Data Release 4 (SDSS DR4),
Kewley \& Ellison published the coefficients of third-order polynomials that
can be used to convert between different strong line metallicity calibrations
for global galaxy spectra. Here, we update the work of Kewley \& Ellison in
three ways. First, by using a newer data release (DR7), we approximately double
the number of galaxies used in polynomial fits, providing statistically
improved polynomial coefficients. Second, we include in the calibration suite
five additional metallicity diagnostics that have been proposed in the last
decade and were not included by Kewley \& Ellison. Finally, we develop a new
machine learning approach for converting between metallicity calibrations. The
random forest algorithm is non-parametric and therefore more flexible than
polynomial conversions, due to its ability to capture non-linear behaviour in
the data. The random forest method yields the same accuracy as the (updated)
polynomial conversions, but has the significant advantage that a single model
can be applied over a wide range of metallicities, without the need to
distinguish upper and lower branches in $R_{23}$ calibrations. The trained
random forest is made publicly available for use in the community.
