论文标题
非参数拉格朗日从神经网的见解中偏见
Non-parametric Lagrangian biasing from the insights of neural nets
论文作者
论文摘要
我们提出了一个Lagrangian的星系聚类偏差模型，在该模型中，我们使用平滑的初始密度场的局部特性来训练神经网，以预测晚期的大规模加权光环场。通过将大规模加权的光晕场拟合在z = 0.5的abacussummit模拟中，我们发现包括三个粗两个粗间的平滑尺度，可以最佳地恢复光晕功率谱。添加更多的平滑尺度可能会导致大规模功率低估2-5％，并可能导致神经网过度拟合。我们发现，在原始高维特征空间中的两个方向可以很好地描述拟合的光环与质量比。将原始功能投射到这两个主要组件中，并重新训练神经网可以重现原始训练结果，或者以更好的光环功率谱匹配来胜过它。主要组件的元素不太可能被分配给物理含义，部分原因是特征在不同的平滑尺度之间高度相关。我们的工作说明了在研究星系偏见时可能需要包括多个平滑量表的潜力，并且可以使用机器学习方法轻松完成，这些方法可以在高维输入特征空间中使用。
We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field. By fitting the mass-weighted halo field in the AbacusSummit simulations at z=0.5, we find that including three coarsely spaced smoothing scales gives the best recovery of the halo power spectrum. Adding more smoothing scales may lead to 2-5% underestimation of the large-scale power and can cause the neural net to overfit. We find that the fitted halo-to-mass ratio can be well described by two directions in the original high-dimension feature space. Projecting the original features into these two principal components and re-training the neural net either reproduces the original training result, or outperforms it with a better match of the halo power spectrum. The elements of the principal components are unlikely to be assigned physical meanings, partly owing to the features being highly correlated between different smoothing scales. Our work illustrates a potential need to include multiple smoothing scales when studying galaxy bias, and this can be done easily with machine-learning methods that can take in high dimensional input feature space.
