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ABSTRACT The connection between the escape fraction of ionizing radiation (fesc) and the properties of galaxies, such as stellar mass ($$\rm M_{\rm *}$$), age, star-formation rate (SFR), and dust content, are key inputs for reionization models, but many of these relationships remain untested at high redshift. We present an analysis of a sample of 96 $$z$$ ∼ 3 galaxies from the Keck Lyman Continuum Spectroscopic Survey (KLCS). These galaxies have both sensitive Keck/LRIS spectroscopic measurements of the Lyman continuum (LyC) region, and multiband photometry that places constraints on stellar population parameters. We construct composite spectra from subsamples binned as a function of galaxy property and quantify the ionizing-photon escape for each composite. We find a significant anti-correlation between fesc and $$\rm M_{\rm *}$$, consistent with predictions from cosmological zoom-in simulations. We also find significant anti-correlation between fesc and E(B−V), encoding the underlying physics of LyC escape in our sample. We also find no significant correlation between fesc and either stellar age or specific SFR (= SFR/$$\rm M_{\rm *}$$), challenging interpretations that synchronize recent star formation and favorable conditions for ionizing escape. The galaxy properties now shown to correlate with fesc in the KLCS are Lyα equivalent width, UV Luminosity, $$\rm M_{\rm *}$$, SFR, and E(B−V), but not age or sSFR. This comprehensive analysis of galaxy properties and LyC escape at high redshift will be used to guide future models and observations of the reionization epoch.
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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:
- 10474353
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- Research Notes of the AAS
- Volume:
- 7
- Issue:
- 11
- ISSN:
- 2515-5172
- Format(s):
- Medium: X Size: Article No. 242
- Size(s):
- Article No. 242
- Sponsoring Org:
- National Science Foundation
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