Cosmic-ray antimatter, particularly low-energy antideuterons, serves as a sensitive probe of dark matter annihilating in our Galaxy. We study this smoking-gun signature and explore its complementarity with indirect dark matter searches using cosmic-ray antiprotons. To this end, we develop the neural network emulator D̅arkRayNet, enabling a fast prediction of propagated antideuteron energy spectra for a wide range of annihilation channels and their combinations. We revisit the Monte Carlo simulation of antideuteron coalescence and cosmic-ray propagation, allowing us to explore the uncertainties of both processes. In particular, we take into account uncertainties from the Λb production rate and consider two distinctly different propagation models. Requiring consistency with cosmic-ray antiproton limits, we find that AMS-02 shows sensitivity to a few windows of dark matter masses only, most prominently below 20 GeV. This region can be probed independently by the upcoming GAPS experiment. The program package D̅arkRayNet is available on GitHub, https://github.com/kathrinnp/DarkRayNet.

中文翻译：

---

D̅arkRayNet：模拟来自暗物质的宇宙射线反氘通量


宇宙射线反物质，特别是低能量反氘子，是银河系中暗物质湮灭的敏感探测器。我们研究了这个冒烟枪的特征，并探索了它与使用宇宙射线反质子的间接暗物质搜索的互补性。为此，我们开发了神经网络仿真器 D̅arkRayNet，能够快速预测各种湮灭通道及其组合的传播反氘能谱。我们重新审视了反氘聚结和宇宙射线传播的蒙特卡洛模拟，使我们能够探索这两个过程的不确定性。特别是，我们考虑了 Λb 产生速率的不确定性，并考虑了两种截然不同的传播模型。要求与宇宙射线反质子极限保持一致，我们发现 AMS-02 仅对暗物质质量的几个窗口表现出敏感性，最突出的是低于 20 GeV。该区域可以通过即将到来的 GAPS 实验独立探测。程序包 D̅arkRayNet 可在 GitHub 上获得，https://github.com/kathrinnp/DarkRayNet。