Identifying reionization-epoch galaxies with extreme levels of Lyman continuum leakage in James Webb Space Telescope surveys
Identifying reionization-epoch galaxies with extreme levels of Lyman continuum leakage in James Webb Space Telescope surveys
复制标题
在詹姆斯·韦伯太空望远镜巡天中识别具有极端莱曼连续泄漏水平的再电离时代星系
DOI:
10.1093/mnras/stz3416
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发表时间:
2019
影响因子:
4.8
通讯作者:
Ruben Cubo
中科院分区:
文献类型:
--
作者:
S. Giri;E. Zackrisson;C. Binggeli;K. Pelckmans;Ruben Cubo
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 ≳ 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 (fesc) of galaxies brighter than mAB,1500 ≈ 27 mag may be retrieved with mean absolute error Δfesc ≈ 0.09(0.12) for 24 h (1.5 h) JWST/NIRSpec exposures at resolution R = 100. For 24 h exposure time, even fainter galaxies (mAB,1500 < 28.5 mag) can be processed with Δfesc ≈ 0.14. This framework simultaneously estimates the redshift of these galaxies with a relative error less than 0.03 for both 24 (mAB,1500 < 28.5 mag) and 1.5 h (mAB,1500 < 27 mag) exposure times. We also consider scenarios where just a minor fraction of galaxies attain high fesc and present the conditions required for detecting a subpopulation of high-fesc galaxies within the data set.