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Using four different suites of cosmological simulations, we generate synthetic spectra for galaxies with different Lyman-continuum escape fractions (fesc) at redshifts –9, in the rest-frame wavelength range relevant for the James Webb Space Telescope (JWST) NIRSpec instrument. By investigating the effects of realistic star formation histories and metallicity distributions on the EW(Hβ)–β diagram (previously proposed as a tool for identifying galaxies with very high fesc), we find that neither of these effects are likely to jeopardize the identification of galaxies with extreme Lyman-continuum leakage. Based on our models, we expect that essentially all galaxies that exhibit rest-frame Å to have . Incorrect assumptions concerning the ionizing fluxes of stellar populations or the dust properties of galaxies can in principle bias the selection, but substantial model deficiencies of this type should at the same time be evident from offsets in the observed distribution of galaxies in the EW(Hβ)–β diagram compared to the simulated distribution. Such offsets would thereby allow JWST/NIRSpec measurements of these observables to serve as input for further model refinement. [ABSTRACT FROM AUTHOR]

Recent observations of galaxies at , 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 for spectra with signal-to-noise ratio ≈ 5 at a rest-frame wavelength of 1500 Å for our fiducial simulation. This prediction accuracy represents a significant improvement over previous similar approaches. [ABSTRACT FROM AUTHOR]