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Cue:一个快速且灵活的光致电离仿真器,用于模拟几乎由任何电离源驱动的星云发射

Cue: A Fast and Flexible Photoionization Emulator for Modeling Nebular Emission Powered By Almost Any Ionizing Source

Yijia Li, Joel Leja, Benjamin D. Johnson, Sandro Tacchella, Rebecca Davies, Sirio Belli, Minjung Park, Razieh Emami

AI总结:

本文提出基于 CLOUDY 的神经网络光致电离仿真器 Cue,用4段幂律灵活表示电离光谱并预测星云连续谱与发射线,可在高信噪比下约束并区分恒星电离源。

摘要:

支配星系中星云发射的复杂物理过程,尤其是在早期宇宙中,往往难以用简单的低维模型描述。这已被证明是理解驱动此类发射的(通常多样的)电离源的一个重大障碍。我们提出 Cue,这是一个高度灵活的工具,用于解释不同红移星系在广泛丰度和电离条件下的星云发射。不同于通常用于解释河外星云发射的典型星云模型,我们的模型不需要特定的电离光谱作为源,而是用一个4段分段幂律来近似电离光谱。我们训练了一个基于 CLOUDY 光致电离建模代码的神经网络仿真器,并作出自洽的星云连续谱和发射线预测。除了灵活的电离光谱外,我们还允许自由设置 [O/H]、[N/O]、[C/O]、气体密度以及总电离光子预算。这种灵活性使我们能够对入射电离辐射进行边缘化处理或直接测量,从而通过遥远星系的星云发射直接探究其中电离光子的来源。我们的仿真器表现出很高的准确性,在预测星云连续谱时具有约1%的不确定度,在预测发射线时具有约5%的不确定度。模拟测试表明,Cue 具有良好的校准性;当 S/N(Hα) ≳ 10 时,它能对电离光谱产生有用的约束,并且还能够区分单星和双星恒星模型所预测的电离光谱。神经网络的计算效率有助于 Cue 未来应用于大样本星云发射的快速建模以及 Monte Carlo 采样技术。

英文摘要:

The complex physics governing nebular emission in galaxies, particularly in the early universe, often defy simple low-dimensional models. This has proven to be a significant barrier in understanding the (often diverse) ionizing sources powering this emission. We present Cue, a highly flexible tool for interpreting nebular emission across a wide range of abundances and ionizing conditions of galaxies at different redshifts. Unlike typical nebular models used to interpret extragalactic nebular emission, our model does not require a specific ionizing spectrum as a source, instead approximating the ionizing spectrum with a 4-part piece-wise power-law. We train a neural net emulator based on the CLOUDY photoionization modeling code and make self-consistent nebular continuum and line emission predictions. Along with the flexible ionizing spectra, we allow freedom in [O/H], [N/O], [C/O], gas density, and total ionizing photon budget. This flexibility allows us to either marginalize over or directly measure the incident ionizing radiation, thereby directly interrogating the source of the ionizing photons in distant galaxies via their nebular emission. Our emulator demonstrates a high accuracy, with $\sim$1% uncertainty in predicting the nebular continuum and $\sim$5% uncertainty in the emission lines. Mock tests suggest Cue is well-calibrated and produces useful constraints on the ionizing spectra when $S/N (\mathrm{H}_α) \gtrsim 10$, and furthermore capable of distinguishing between the ionizing spectra predicted by single and binary stellar models. The compute efficiency of neural networks facilitates future applications of Cue for rapid modeling of the nebular emission in large samples and Monte Carlo sampling techniques.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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