模拟星系发射线运动学的一个自我监督的、物理感知的贝叶斯神经网络结构
A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics
摘要：
在未来的几十年里，像SKA这样的大型设施将提供数百万星系运动学的解析观测。为了帮助及时利用这些庞大的数据集，我们探索使用一个自我监督，物理意识的神经网络能够贝叶斯运动学建模的星系。我们展示了网络的能力，模拟星系中的冷气体的运动学，重点是恢复物理参数和伴随的建模误差。该模型能够恢复旋转曲线，倾角和光盘尺度长度的CO和HI数据匹配以及与文献中发现的。该模型还能够提供建模误差超过学习的参数由于准贝叶斯蒙特卡罗辍学的应用。这项工作显示了机器学习的前景，特别是自我监督的神经网络，在运动学建模的背景下星系。这项工作代表了将这些模型应用于运动学拟合的第一步，我们建议我们的模型的变体似乎特别适合于从即将到来的调查中实现排放线科学，例如SKA，允许快速利用这些大型数据集。
英文摘要：
In the upcoming decades large facilities, such as the SKA, will provide resolved observations of the kinematics of millions of galaxies. In order to assist in the timely exploitation of these vast datasets we explore the use of a self-supervised, physics aware neural network capable of Bayesian kinematic modelling of galaxies. We demonstrate the network's ability to model the kinematics of cold gas in galaxies with an emphasis on recovering physical parameters and accompanying modelling errors. The model is able to recover rotation curves, inclinations and disc scale lengths for both CO and HI data which match well with those found in the literature. The model is also able to provide modelling errors over learned parameters thanks to the application of quasi-Bayesian Monte-Carlo dropout. This work shows the promising use of machine learning, and in particular self-supervised neural networks, in the context of kinematically modelling galaxies. This work represents the first steps in applying such models for kinematic fitting and we propose that variants of our model would seem especially suitable for enabling emission-line science from upcoming surveys with e.g. the SKA, allowing fast exploitation of these large datasets.
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。
