A machine-learning approach to measuring the escape of ionizing
  radiation from galaxies in the reionization epoch
  Recent observations of galaxies at $z \gtrsim 7$, along with the low value of
the electron scattering optical depth measured by the Planck mission, make
galaxies plausible as dominant sources of ionizing photons during the epoch of
reionization. However, scenarios of galaxy-driven reionization hinge on the
assumption that the average escape fraction of ionizing photons is
significantly higher for galaxies in the reionization epoch than in the local
Universe. The NIRSpec instrument on the James Webb Space Telescope (JWST) will
enable spectroscopic observations of large samples of reionization-epoch
galaxies. While the leakage of ionizing photons will not be directly measurable
from these spectra, the leakage is predicted to have an indirect effect on the
spectral slope and the strength of nebular emission lines in the rest-frame
ultraviolet and optical. Here, we apply a machine learning technique known as
lasso regression on mock JWST/NIRSpec observations of simulated $z=7$ galaxies
in order to obtain a model that can predict the escape fraction from
JWST/NIRSpec data. Barring systematic biases in the simulated spectra, our
method is able to retrieve the escape fraction with a mean absolute error of
$\Delta f_{\mathrm{esc}} \approx 0.12$ for spectra with $S/N\approx 5$ at a
rest-frame wavelength of 1500 {\AA} for our fiducial simulation. This
prediction accuracy represents a significant improvement over previous similar
approaches.
