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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.03997 |
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Table of Contents:
- The JWST and ALMA have detected emission lines from the ionized interstellar medium (ISM) in some of the first galaxies at $z \gtrsim 6$. These measurements present an opportunity to better understand galaxy assembly histories and may allow important tests of state-of-the-art galaxy formation simulations. It is challenging, however, to model these lines in their proper cosmological context. In order to meet this challenge, we introduce a novel sub-grid line emission modeling framework. The framework uses the high-$z$ zoom-in simulation suite from the Feedback in Realistic Environments (FIRE) collaboration. The line emission signals from HII regions within each simulated FIRE galaxy are modeled using the semi-analytic HIILines code. A machine learning approach is then used to determine the conditional probability distribution for the line luminosity to stellar-mass ratio from the HII regions around each simulated stellar particle. This conditional probability distribution can then be applied to predict the line luminosities around stellar particles in lower resolution, yet larger volume cosmological simulations. As an example, we apply this approach to the IllustrisTNG simulations at $z=6$. The resulting predictions for the [OII], [OIII], and Balmer line luminosities as a function of star-formation rate (SFR) agree well with current observations. Our predictions differ, however, from related work in the literature which lack detailed sub-grid ISM models. This highlights the importance of our multi-scale simulation modeling framework. Finally, we provide forecasts for future line luminosity function measurements from the JWST and quantify the cosmic variance in such surveys.