超弱相互作用暗物质的指纹匹配：神经网络方法
Fingerprint matching of beyond-WIMP dark matter: neural network approach
摘要：
银河系尺度的结构是特别感兴趣的，因为它提供了重要的线索，暗物质的属性和它的观测正在改善。弱相互作用大质量粒子（WIMP）在星系尺度上表现为冷暗物质，而超WIMP候选者抑制星系尺度结构的形成。线性物质功率谱中的抑制通常由单一参数表征，即热暖暗物质质量。另一方面，抑制的形式取决于潜在的机制。有必要引入多个参数以覆盖广泛的beyond-WIMP模型。一旦引入多个参数，从一方到另一方共享结果就变得更加困难。在这项工作中，我们建议采用神经网络技术，以促进双方之间的沟通。为了演示如何以一种具体的方式工作，我们考虑一个简化的模型，轻弱相互作用的大质量粒子。
英文摘要：
Galactic-scale structure is of particular interest since it provides important clues to dark matter properties and its observation is improving. Weakly interacting massive particles (WIMPs) behave as cold dark matter on galactic scales, while beyond-WIMP candidates suppress galactic-scale structure formation. Suppression in the linear matter power spectrum has been conventionally characterized by a single parameter, the thermal warm dark matter mass. On the other hand, the shape of suppression depends on the underlying mechanism. It is necessary to introduce multiple parameters to cover a wide range of beyond-WIMP models. Once multiple parameters are introduced, it becomes harder to share results from one side to the other. In this work, we propose adopting neural network technique to facilitate the communication between the two sides. To demonstrate how to work out in a concrete manner, we consider a simplified model of light feebly interacting massive particles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
