We present a neural network emulator for baryonic effects in the non-linear matter power spectrum. We calibrate this emulator using more than 50 000 measurements in a 15D parameter space, varying cosmology and baryonic physics. Baryonic physics is described through a baryonification algorithm, which has been shown to accurately capture the relevant effects on the power spectrum and bispectrum in state-of-the-art hydrodynamical simulations. Cosmological parameters are sampled using a cosmology-rescaling approach including massive neutrinos and dynamical dark energy. The specific quantity we emulate is the ratio between matter power spectrum with baryons and gravity only, and we estimate the overall precision of the emulator to be $2\!-\!3{{\ \rm per\ cent}}$, at scales $k \lt 5 \, h\, {\rm Mpc}^{-1}$ and redshifts 0 < z < 1.5. We obtain an accuracy of $1\!-\!2{{\ \rm per\ cent}}$, when testing the emulator against a collection of 74 different cosmological hydrodynamical simulations and their respective gravity-only counterparts. We also show that only one baryonic parameter, namely Mc, which sets the gas fraction retained per halo mass, is enough to have accurate predictions of most of the baryonic feedbacks at a given epoch. Our emulator is publicly available at http://www.dipc.org/bacco.

中文翻译：

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BACCO 模拟项目：带有神经网络的重子化模拟器

我们提出了一个神经网络模拟器，用于非线性物质功率谱中的重子效应。我们在 15D 参数空间、不同的宇宙学和重子物理学中使用超过 50,000 次测量来校准这个模拟器。重子物理通过重子化算法进行描述，该算法已被证明可以在最先进的流体动力学模拟中准确捕捉对功率谱和双谱的相关影响。宇宙学参数使用宇宙学重新缩放方法进行采样，包括大量中微子和动态暗能量。我们模拟的具体量是重子的物质功率谱与仅重力之间的比率，我们估计模拟器的整体精度为 $2\!-\!3{{\ \rm per\ cent}}$，在尺度上$k \lt 5 \, h\, {\rm Mpc}^{-1}$ 和红移 0 < z＜1.5。当针对 74 种不同的宇宙流体动力学模拟及其各自的仅重力模拟对模拟器进行测试时，我们获得了 $1\!-\!2{{\ \rm per\cent}}$ 的准确度。我们还表明，只有一个重子参数，即 Mc，它设置每个晕质量保留的气体分数，足以准确预测给定时期的大多数重子反馈。我们的模拟器在 http://www.dipc.org/bacco 上公开可用。足以准确预测给定时期的大多数重子反馈。我们的模拟器在 http://www.dipc.org/bacco 上公开可用。足以准确预测给定时期的大多数重子反馈。我们的模拟器在 http://www.dipc.org/bacco 上公开可用。