论文标题

加薪:基于仿真的AGN喷气机和裂片的分析模型

RAiSE: simulation-based analytical model of AGN jets and lobes

论文作者

Turner, Ross J., Yates-Jones, Patrick M., Shabala, Stanislav S., Quici, Benjamin, Stewart, Georgia S. C.

论文摘要

我们提出了一个分析模型,用于整个生命周期中扩展活性银河核(AGN)的演变,包括初始的射流膨胀,叶的形成和最终的残留相。我们贡献的一个特殊重点是早期喷气膨胀阶段,传统上在分析模型中并不能很好地捕获。我们在无线电AGN中在半分析环境(升高)框架中实现了此模型,并发现预测的无线电源动力学与低功率的Fanaroff-Riley类型I和高功率的II型无线电裂片的流体动力模拟非常吻合。我们通过用磁场和冲击加速历史构造合成同步器表面亮度图像,该模型是从现有的流体动力模拟中获取的一组拉格朗日示踪剂颗粒的冲击加速历史。我们表明,一组颗粒足以对具有非常不同的射流参数和环境密度曲线正常化的Fanaroff-Riley型II无线电裂片的动力学和可观察的特征进行准确描述。我们的新模型预测,年轻(<10 Myr)来源的裂片将比现有的分析模型在相同年龄的年龄更长,更明亮,而现有分析模型缺乏喷射为主的扩张阶段。这一发现对解释射电观测的解释具有重要意义。 Github和Pypi上公开可用的RAISY代码,用Python撰写。

We present an analytical model for the evolution of extended active galactic nuclei (AGNs) throughout their full lifecycle, including the initial jet expansion, lobe formation, and eventual remnant phases. A particular focus of our contribution is on the early jet expansion phase, which is traditionally not well captured in analytical models. We implement this model within the Radio AGN in Semi-Analytic Environments (RAiSE) framework, and find that the predicted radio source dynamics are in good agreement with hydrodynamic simulations of both low-powered Fanaroff-Riley Type-I and high-powered Type-II radio lobes. We construct synthetic synchrotron surface brightness images by complementing the original RAiSE model with the magnetic field and shock-acceleration histories of a set of Lagrangian tracer particles taken from an existing hydrodynamic simulation. We show that a single set of particles is sufficient for an accurate description of the dynamics and observable features of Fanaroff-Riley Type-II radio lobes with very different jet parameters and ambient density profile normalisations. Our new model predicts that the lobes of young (< 10 Myr) sources will be both longer and brighter than expected at the same age from existing analytical models which lack a jet-dominated expansion phase; this finding has important implications for interpretation of radio galaxy observations. The RAiSE code, written in Python, is publicly available on GitHub and PyPI.

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