arXiv 2203.047822022-03astro-ph.GA

从化学演化模型预测星际辐射场

Predicting interstellar radiation fields from chemical evolution models

Mario Romero, Pablo Corcho-Caballero, Iker Millán-Irigoyen, Mercedes Mollá, Yago Ascasibar

备注：: 16 pages, 7 figures, 3 tables. Accepted for publication in MNRAS
DOI：: 10.1093/mnras/stad454

摘要：

基于类银河系的两种化学演化模型，提出了从极紫外到亚毫米波段的星际辐射场的自洽预测.为此，我们开发了一个新的工具，称为Mixclask，包括气体的发射，吸收和散射的光电离代码云到蒙特卡罗辐射转移代码裙。迭代地调用这两种算法，直到ISM的物理属性收敛。我们设计了第一个测试，让人想起HII区域，我们发现Mixclask的结果与球对称多云模拟吻合得很好。这两个MWG模型的基础上的化学演化代码给出的结果与以前的经验模型在文献中报道的我们的银河系的ISRF大致一致，尽管他们系统地低估了中红外发射。我们还发现两个模型在整个紫外线范围内存在显着差异，这在之前的ISRF模型中没有得到充分探讨。这些结果表明，我们的方法相结合的辐射转移与化学演化模型的可行性：有增加的预测能力和星际辐射场得到的模型参数提供了进一步的约束。实现我们方法的Python源代码可在https://github.com/MarioRomeroC/Mixclask上公开获得。

英文摘要：

We present a self-consistent prediction of the interstellar radiation field (ISRF), from the extreme ultraviolet (EUV) to sub-mm range, based on two chemical evolution models of a Milky Way-like galaxy (MWG). To this end, we develop a new tool called Mixclask to include gas emission, absorption and scattering from the photoionization code Cloudy into the Monte Carlo radiative transfer code Skirt. Both algorithms are invoked iteratively, until the physical properties of the ISM converge. We have designed a first test, reminiscent of a HII region, and we find that the results of Mixclask are in good agreement with a spherically symmetric Cloudy simulation. Both MWG models based on chemical evolution codes give results broadly consistent with previous empirical models reported in the literature for the ISRF of our Galaxy, albeit they systematically underestimate the mid-infrared emission. We also find significant differences between our two models in the whole ultraviolet range, not fully explored in previous ISRF models. These results show the feasibility of our method of combining radiative transfer with chemical evolution models: there is increased predictive power and the interstellar radiation field obtained provides further constraints on the model parameters. Python source code to implement our method is publicly available at https://github.com/MarioRomeroC/Mixclask.

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