AI 中文总结
研究如何从法拉第旋转获取河外磁场，通过对星系环境模拟观测探索DM估计器性能，用特定估计器获最佳结果，指数因环境而异，基于测试给出提取磁场的最佳实践，可精确测量磁场，助力利用SKA下一代调查的RM数据。
AI 中文摘要
法拉第旋转量（RMs）是测量河外系统磁场强度的少数观测工具之一，但将RM转换为磁场估计值需要知道到RM源的电子柱密度，即色散量（DM）。由于大多数河外射电星系的DM难以测量，观测者采用了一系列策略从更易测量的量来估计它们，但其准确性未知。为解决此问题，我们对一系列星系环境的高分辨率磁流体动力学模拟进行了模拟观测，以探索各种可能的DM估计器的性能。我们使用估计器\(\mathrm{DM} \propto \mathrm{EM}^{\alpha} \ N_\mathrm{Hi}^{\beta}\)获得了最佳结果，其中EM是发射量，\(N_\mathrm{Hi}\)是原子氢柱密度，指数\(\alpha \approx 0.2 - 0.4\)和\(\beta \approx 0 - 0.1\)取决于星系环境。我们表明这些指数随环境的变化可用简单物理论据解释。基于测试，我们提供了根据星系环境和代理数据可用性从RM数据中提取星系磁场的推荐最佳实践，并表明使用这些方法可获得精确到十分之几dex的磁场测量值。这项工作是利用来自SKA的下一代调查的RM数据的重要一步。
英文摘要
Faraday rotation measures (RMs) are one of our few observational tools for measuring magnetic field strengths in extragalactic systems, but converting an RM to a magnetic field estimate requires knowledge of the electron column density -- the dispersion measure (DM) -- to the RM source. Because DMs are difficult to measure for most extragalactic radio galaxies, observers have adopted a range of strategies to estimate them from more easily measured quantities, but the accuracy of these approaches is poorly known. To address this, we carry out simulated observations of high-resolution magnetohydrodynamic simulations of a range of galactic environments to explore the performance of various possible DM estimators. We obtain the best results using an estimator $\mathrm{DM} \propto \mathrm{EM}^α \ N_\mathrm{Hi}^β$, where EM is the emission measure and $N_\mathrm{Hi}$ is the atomic hydrogen column density, with exponents $α\approx 0.2-0.4$ and $β\approx 0-0.1$ depending on galactic environment (e.g., galaxy centres versus outskirts, and dwarfs versus spirals). We show that the variation of these exponents with environment can be understood in terms of simple physical arguments. Based on our tests, we provide recommended best practices for extracting galactic magnetic fields from RM data as a function of galactic environment and of proxy data availability, and show that using these methods one can obtain field measurements that are accurate to a few tenths of a dex. This work therefore represents an important step toward making use of RM data from next-generation surveys with the SKA.
Comments22 pages, 10 figures, under review MNRAS
