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 –10. To make the samples more realistic, we combine synthetic galaxy spectra based on cosmological galaxy simulations with observational noise relevant for 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 () of galaxies brighter than mag may be retrieved with mean absolute error $\Delta f_\approx$0.09(0.12) for 24h (1.5h) JWST/NIRSpec exposures at resolution R=100. For 24h exposure time, even fainter galaxies ( mag) can be processed with $\Delta f_\approx$0.14. This framework simultaneously estimates the redshift of these galaxies with a relative error less than 0.03 for both 24h ( mag) and 1.5h ( mag) exposure times. We also consider scenarios where just a minor fraction of galaxies attain high and present the conditions required for detecting a subpopulation of high galaxies within the dataset.

生成讲解博客的中文版本

生成AI智能校验的中文版本