论文标题
在没有监督的情况下解码暗物质子结构
Decoding Dark Matter Substructure without Supervision
论文作者
论文摘要
暗物质的身份仍然是当今物理学中最紧迫的问题之一。尽管在过去的半个世纪中已经提出了许多有前途的暗物质候选人，但迄今为止，暗物质的真实身份仍然难以捉摸。虽然可能被认为是暗物质的众多候选人之一，但至少有可能尚未提出正确的物理描述。为了应对这一挑战，机器学习的新应用可以帮助物理学家从理论的不可知论的角度洞悉黑暗部门。在这项工作中，我们证明了使用无监督的机器学习技术使用Galaxy-Galaxy强透镜模拟来推断暗物质晕中的子结构的存在。
The identity of dark matter remains one of the most pressing questions in physics today. While many promising dark matter candidates have been put forth over the last half-century, to date the true identity of dark matter remains elusive. While it is possible that one of the many proposed candidates may turn out to be dark matter, it is at least equally likely that the correct physical description has yet to be proposed. To address this challenge, novel applications of machine learning can help physicists gain insight into the dark sector from a theory agnostic perspective. In this work we demonstrate the use of unsupervised machine learning techniques to infer the presence of substructure in dark matter halos using galaxy-galaxy strong lensing simulations.
