Theoretical uncertainty limits our ability to extract cosmological information from baryonic fields such as the thermal Sunyaev-Zel'dovich (tSZ) effect. Being sourced by the electron pressure field, the tSZ effect depends on baryonic physics that is usually modeled by expensive hydrodynamic simulations. We train neural networks on the IllustrisTNG-300 cosmological simulation to predict the continuous electron pressure field in galaxy clusters from gravity-only simulations. Modeling clusters is challenging for neural networks as most of the gas pressure is concentrated in a handful of voxels and even the largest hydrodynamical simulations contain only a few hundred clusters that can be used for training. Instead of conventional convolutional neural net (CNN) architectures, we choose to employ a rotationally equivariant DeepSets architecture to operate directly on the set of dark matter particles. We argue that set-based architectures provide distinct advantages over CNNs. For example, we can enforce exact rotational and permutation equivariance, incorporate existing knowledge on the tSZ field, and work with sparse fields as are standard in cosmology. We compose our architecture with separate, physically meaningful modules, making it amenable to interpretation. For example, we can separately study the influence of local and cluster-scale environment, determine that cluster triaxiality has negligible impact, and train a module that corrects for mis-centering. Our model improves by 70 % on analytic profiles fit to the same simulation data. We argue that the electron pressure field, viewed as a function of a gravity-only simulation, has inherent stochasticity, and model this property through a conditional-VAE extension to the network. This modification yields further improvement by 7 %, it is limited by our small training set however. (abridged)
翻译:理论上的不确定性限制了我们从热 Sunyaev- Zel'dovich (tSZ) 效应等气流场中提取宇宙信息的能力。 tSZ 效应来源于电子压力场, 取决于通常由昂贵流体动力模拟模型模拟的气态物理学。 我们在IllustrisTNG-300宇宙模拟中培养神经网络, 以预测星系群中连续的电子压力场, 从重力模拟中预测。 模拟集群对于神经网络来说具有挑战性, 因为大部分气体压力集中在少量的蒸气中, 甚至最大的流体动力模拟只包含几百个可用于培训的电流力阵列。 我们选择在IllustisTNG-300宇宙模拟中进行神经网络的旋转和变异性化网络网络。 但是, 定置的电流结构提供了比CNN更显著的优势。 例如, 我们可以实施精确的旋转和变异性, 将现有知识融入到 tSZ 模型场, 甚至最大的水力模拟中只有几百个可用作训练的电流。 我们的电流数据流流流流流流流流流流流流流流到独立的模型, 我们的模型的模型可以用来进行独立的模型, 我们的电流化的模型可以用来进行真正的模型, 以不同的分析, 以不同的模型来决定我们的物理模型 。