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We present estimates of the ultraviolet (UV) and Lyman continuum flux density contributed by galaxies of luminosities from to at redshifts 5≤z≤10 using a galaxy formation model that reproduces properties of local dwarf galaxies down to the luminosities of the ultra-faint satellites. We characterize the UV luminosity function (LF) of galaxies and their abundance as a function of the ionizing photon emission rate predicted by our model and present accurate fitting functions describing them. Although the slope of the LF becomes gradually shallower with decreasing luminosity due to feedback-driven outflows, the UV LF predicted by the model remains quite steep at the luminosities . After reionization, the UV LF flattens at due to UV heating of intergalactic gas. However, before reionization, the slope of the LF remains steep and approximately constant from to . We show that for a constant ionizing photon escape fraction the contribution of faint galaxies with to the UV flux and ionizing photon budget is ≈40−60% at z>7 and decreases to ≈20% at z=6. Before reionization, even ultra-faint galaxies of contribute ≈10−25% of ionizing photons. If the escape fraction increases strongly for fainter galaxies, the contribution of galaxies before reionization increases to ≈60−75%. Our results imply that dwarf galaxies fainter than , beyond the James Webb Space Telescope limit, contribute significantly to the UV flux density and ionizing photon budget before reionization alleviating requirements on the escape fraction of Lyman continuum photons.
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Predicting the UV Escape Fraction of the First Galaxies in the Renaissance Simulations with Machine Learning
Abstract Cosmic reionization is likely driven by UV starlight emanating from the first generations of galaxies. A galaxy’s UV escape fraction, or the fraction of photons escaping from the galaxy, is useful to quantify its contribution to reionization. However, the UV escape fraction is notoriously difficult to predict due to local environment dependency and variability over time. Using data from the Renaissance Simulations, we attempt to make predictions about the impact of the first stars and galaxies on their environments. We present a time-independent classification model using a general artificial neural network architecture to predict the UV escape fraction given other galaxy properties—namely halo mass, stellar mass, redshift, star formation rate, lookback time, and gas fraction. We find our validation accuracy to be approximately 50%–65%, depending on the data set size from each zoom-in region of the Renaissance Simulations.
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- PAR ID:
- 10480074
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Research Notes of the AAS
- Volume:
- 7
- Issue:
- 11
- ISSN:
- 2515-5172
- Page Range / eLocation ID:
- 242
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.3847/2515-5172/ad0cc5
