基于机器学习和符号回归的装配偏差建模
Modeling assembly bias with machine learning and symbolic regression
摘要：
即将进行的21厘米测量将绘制宇宙中性氢（HI）在前所未有的体积上的空间分布。需要模拟目录来充分利用这些调查的潜力。用于创建这些模拟目录的标准技术，如Halo Occupation Distribution（HOD），依赖于诸如暗物质晕的重子性质仅取决于其质量的假设。在这项工作中，我们使用国家的最先进的磁流体动力学模拟IllustrisTNG表明，HI含量的晕表现出强烈的依赖于当地的环境。然后，我们使用机器学习技术来证明这种效应可以1）通过这些算法建模，2）以新颖的解析方程的形式参数化。我们提供了这种环境效应的物理解释，并表明，忽略它会导致预测不足的真实空间21厘米功率谱在$k\gtrsim 0.05$ h/Mpc的$\gtrsim$ 10\%，这是大于预期的精度从即将到来的调查，这样的大规模。我们将数值模拟与机器学习技术相结合的方法是通用的，并开辟了一个新的方向，为星系和线强度映射调查生成准确模拟所需的组装偏差的复杂物理建模和参数化。
英文摘要：
Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over unprecedented volumes. Mock catalogues are needed to fully exploit the potential of these surveys. Standard techniques employed to create these mock catalogs, like Halo Occupation Distribution (HOD), rely on assumptions such as the baryonic properties of dark matter halos only depend on their masses. In this work, we use the state-of-the-art magneto-hydrodynamic simulation IllustrisTNG to show that the HI content of halos exhibits a strong dependence on their local environment. We then use machine learning techniques to show that this effect can be 1) modeled by these algorithms and 2) parametrized in the form of novel analytic equations. We provide physical explanations for this environmental effect and show that ignoring it leads to underprediction of the real-space 21-cm power spectrum at $k\gtrsim 0.05$ h/Mpc by $\gtrsim$10\%, which is larger than the expected precision from upcoming surveys on such large scales. Our methodology of combining numerical simulations with machine learning techniques is general, and opens a new direction at modeling and parametrizing the complex physics of assembly bias needed to generate accurate mocks for galaxy and line intensity mapping surveys.
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