picasso气体模型：在仅引力模拟上绘制星系团内气体
The picasso gas model: Painting intracluster gas on gravity-only simulations
AI总结：
提出picasso模型，结合解析气体模型与机器学习，基于仅引力模拟的晕特性预测星系团内介质热力学性质，实现高精度预测并开源。
摘要：
我们引入了picasso，这是一个旨在基于仅引力模拟中晕的特性来预测星系团内介质热力学性质的模型。该预测结合了将气体特性映射到引力势的解析气体模型，以及基于晕的标量特性（如质量和浓度）预测单个晕模型参数的机器学习模型。训练完成后，该模型可应用于对任意势分布进行预测，允许其与灵活的输入（如N体粒子分布或径向剖面）配合使用。我们展示了该模型，并使用仅引力模拟和流体动力学模拟的配对对其进行训练。我们表明，当训练学习从仅引力模拟到非辐射流体动力学模拟的映射时，picasso能够对星系团内气体热力学做出极其准确和精确的预测，在$r / R_{500c} \in [0.1,1]$范围内具有百分比级别的偏差和约$20\\%$的离散度。在包含亚分辨率物理建模的流体动力学模拟上训练该模型也能产生稳健的预测，尽管会引入依赖于半径的偏差并增加离散度。我们进一步表明，该模型可以经过训练，从非常少的晕信息（低至质量和浓度）中做出准确预测，代价是精度略有降低。picasso作为Python包在https://github.com/fkeruzore/picasso公开提供，其中包含训练好的模型，可以在完全自动可微和硬件加速的框架中轻松高效地进行预测。
英文摘要：
We introduce picasso, a model designed to predict thermodynamic properties of the intracluster medium based on the properties of halos in gravity-only simulations. The predictions result from the combination of an analytical gas model, mapping gas properties to the gravitational potential, and of a machine learning model to predict the model parameters for individual halos based on their scalar properties, such as mass and concentration. Once trained, the model can be applied to make predictions for arbitrary potential distributions, allowing its use with flexible inputs such as N-body particle distributions or radial profiles. We present the model, and train it using pairs of gravity-only and hydrodynamic simulations. We show that when trained to learn the mapping from gravity-only to non-radiative hydrodynamic simulations, picasso can make remarkably accurate and precise predictions of intracluster gas thermodynamics, with percent-level bias and $\sim 20 \%$ scatter for $r / R_{500c} \in [0.1,1]$. Training the model on hydrodynamic simulations including sub-resolution physics modeling yields robust predictions as well, albeit with the introduction of a radius-dependent bias and an increase in scatter. We further show that the model can be trained to make accurate predictions from very minimal halo information, down to mass and concentration, at the cost of modestly reduced precision. picasso is made publicly available as a Python package at https://github.com/fkeruzore/picasso, which includes trained models that can be used to make predictions easily and efficiently, in a fully auto-differentiable and hardware-accelerated framework
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