Here we present Halo-FDCA, a robust open source Python package for modeling and estimating total flux densities of radio (mini) halos in galaxy clusters. Radio halos are extended ($\sim 200-1500$ kpc in size) synchrotron emitting sources found in galaxy clusters that trace the presence of cosmic rays and magnetic fields in the intracluster medium (ICM). These sources are centrally located and have a low surface brightness. Their exact origin is still unknown but they are likely related to cosmic rays being re-accelerated in-situ by merger or sloshing driven ICM turbulence. The presented algorithm combines the numerical power of the Markov Chain Monte Carlo routine and multiple theoretical models to estimate the total radio flux density of a radio halo from a radio image and its associated uncertainty. This method introduces a flexible analytic fitting procedure to replace existing simplistic manual measurements prone to biases and inaccuracies. It allows to easily determine the properties of the emission and is particularly suitable for future studies of large samples of clusters.

在这里，我们介绍Halo-FDCA，这是一个健壮的开源Python软件包，用于建模和估计星系团中无线电（微型）光环的总通量密度。无线电光晕扩展了（$〜200-1500$星系团中发现的同步加速器发射源，跟踪星团内介质（ICM）中宇宙射线和磁场的存在。这些光源位于中心位置，并且表面亮度较低。它们的确切起源仍是未知的，但它们可能与宇宙射线通过合并或晃动驱动的ICM湍流而在原地重新加速有关。提出的算法结合了马尔可夫链蒙特卡洛例程的数值能力和多个理论模型，可以根据无线电图像及其相关的不确定性来估算无线电晕的总无线电通量密度。这种方法引入了一种灵活的分析拟合程序，以替换现有的易于出现偏差和不准确的简单的手工测量。