So far no diffuse emissions in dwarf spheroidal satellites of the Milky Way have ever been observed. Given that dwarf galaxies are predominantly composed of Dark Matter, the discovery of these signals could offer valuable insights into understanding the nature of Dark Matter. We present “diffSph”, a Python tool which in its present version provides fast predictions of such diffuse signals in radio frequencies. It also features a very comprehensive module for the computation of “J” and “D” factors that are relevant for indirect Dark Matter detection using gamma rays. Routines are coupled to parton-shower algorithms and Dark Matter halo mass functions from state-of-the-art kinematic fits. This code is also useful for testing generic hypotheses (not necessarily associated with any Dark Matter candidate) about the cosmic-ray electron/positron sources in the dwarf galaxies. The diffSph tool has already been employed in searches for diffuse signals from dwarf spheroidal galaxies using the LOw Frequency ARray (LOFAR).

中文翻译：

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diffSph：一种用于计算来自矮球状星系的漫射信号的 Python 工具


到目前为止，在银河系的矮球状卫星中还没有观察到漫射。鉴于矮星系主要由暗物质组成，这些信号的发现可以为理解暗物质的性质提供有价值的见解。我们提出了 “diffSph”，这是一个 Python 工具，在当前版本中，它可以快速预测无线电频率中的此类漫射信号。它还具有一个非常全面的模块，用于计算与使用伽马射线间接探测暗物质相关的 “J” 和 “D” 因子。例程与 parton-shower 算法和暗物质光晕质量函数耦合，来自最先进的运动学拟合。此代码还可用于测试关于矮星系中宇宙射线电子/正电子源的一般假设（不一定与任何暗物质候选者相关）。diffSph 工具已经被用于使用 LOw 频率 ARray （LOFAR） 搜索来自矮球状星系的漫射信号。