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Identifying reionization-epoch galaxies with extreme levels of Lyman continuum leakage in James Webb Space Telescope surveys

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arxiv 1903.01483 v2 pith:KEKKXWOC submitted 2019-03-04 astro-ph.GA

classification astro-ph.GA
keywords galaxiesmathrmgalaxynirspecapproxhighjwstdata
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abstract

The James Webb Space Telescope (JWST) NIRSpec instrument will allow rest-frame ultraviolet/optical spectroscopy of galaxies in the epoch of reionization (EoR). Some galaxies may exhibit significant leakage of hydrogen-ionizing photons into the intergalactic medium, resulting in faint nebular emission lines. We present a machine learning framework for identifying cases of very high hydrogen-ionizing photon escape from galaxies based on the data quality expected from potential NIRSpec observations of EoR galaxies in lensed fields. We train our algorithm on mock samples of JWST/NIRSpec data for galaxies at redshifts $z=6$--10. To make the samples more realistic, we combine synthetic galaxy spectra based on cosmological galaxy simulations with observational noise relevant for $z\gtrsim 6$ objects of a brightness similar to EoR galaxy candidates uncovered in Frontier Fields observations of galaxy cluster Abell-2744 and MACS-J0416. We find that ionizing escape fractions ($f_\mathrm{esc}$) of galaxies brighter than $m_\mathrm{AB,1500} \approx 27$ mag may be retrieved with mean absolute error $\Delta f_\mathrm{esc}\approx$0.09(0.12) for 24h (1.5h) JWST/NIRSpec exposures at resolution R=100. For 24h exposure time, even fainter galaxies ($m_\mathrm{AB,1500} < 28.5$ mag) can be processed with $\Delta f_\mathrm{esc}\approx$0.14. This framework simultaneously estimates the redshift of these galaxies with a relative error less than 0.03 for both 24h ($m_\mathrm{AB,1500} < 28.5$ mag) and 1.5h ($m_\mathrm{AB,1500} < 27$ mag) exposure times. We also consider scenarios where just a minor fraction of galaxies attain high $f_\mathrm{esc}$ and present the conditions required for detecting a subpopulation of high $f_\mathrm{esc}$ galaxies within the dataset.

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