deepCool：用人工神经网络快速精确估算辐照气体冷却速率
deepCool: Fast and Accurate Estimation of Cooling Rates in Irradiated Gas with Artificial Neural Networks
摘要：
精确的辐射冷却模型是现代宇宙学模拟的基本组成部分。如果没有冷却，吸积重子将不能有效地耗散它们的能量，并坍缩到晕的中心形成恒星。众所周知，辐射场的光谱能量分布的幅度和形状的局部变化可以急剧改变冷却速率。在这里，我们介绍deepCool，deepHeat和deepMetal：使用人工神经网络精确建模总冷却速率，总加热速率和金属线仅冷却速率的方法。我们训练我们的算法上的高分辨率宇宙辐射流体动力学模拟和证明，我们可以预测的冷却速率，测量与光电离代码云，在局部辐射场的影响下，~ 5%的精度。我们的方法是计算和内存效率，使其适合部署在国家的最先进的辐射流体动力学模拟。我们发现，环星系介质和弥漫气体周围的星系中心区域的辐射和气体的相互作用的影响最大，标准的冷却功能，忽略了当地的辐射场可以错误地预测冷却速率超过一个数量级，表明星系中的重子循环的影响，当地的辐射场对冷却速率的影响。
英文摘要：
Accurate models of radiative cooling are a fundamental ingredient of modern cosmological simulations. Without cooling, accreted baryons will not efficiently dissipate their energy and collapse to the centres of haloes to form stars. It is well established that local variations in the amplitude and shape of the spectral energy distribution of the radiation field can drastically alter the cooling rate. Here we introduce deepCool, deepHeat, and deepMetal: methods for accurately modelling the total cooling rates, total heating rates, and metal-line only cooling rates of irradiated gas using artificial neural networks. We train our algorithm on a high-resolution cosmological radiation hydrodynamics simulation and demonstrate that we can predict the cooling rate, as measured with the photoionisation code CLOUDY, under the influence of a local radiation field, to an accuracy of ~5%. Our method is computationally and memory efficient, making it suitable for deployment in state-of-the-art radiation hydrodynamics simulations. We show that the circumgalactic medium and diffuse gas surrounding the central regions of a galaxy are most affected by the interplay of radiation and gas, and that standard cooling functions that ignore the local radiation field can incorrectly predict the cooling rate by more than an order of magnitude, indicating that the baryon cycle in galaxies is affected by the influence of a local radiation field on the cooling rate.
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