基于神经网络洞见的非参数拉格朗日偏袒模型
Non-parametric Lagrangian biasing from the insights of neural nets
AI总结：
本文提出一种基于神经网络的非参数拉格朗日偏袒模型，利用 AbacusSummit 模拟发现三个平滑尺度可最佳恢复暗晕功率谱，并证明两个主成分方向足以描述暗晕质量比，表明研究星系偏袒需考虑多平滑尺度。
摘要：
我们提出了一个星系成团性偏袒的拉格朗日模型，在该模型中，我们利用平滑后的初始密度场的局部性质训练一个神经网络，以预测晚期质量加权的暗晕场。通过在 z=0.5 的 AbacusSummit 模拟中拟合质量加权的暗晕场，我们发现包含三个粗间隔的平滑尺度能够最好地恢复暗晕功率谱。增加更多平滑尺度可能导致大尺度功率被低估 2-5%，并且可能使神经网络过拟合。我们发现拟合得到的暗晕质量比可以很好地由原始高维特征空间中的两个方向来描述。将原始特征投影到这两个主成分上并重新训练神经网络，要么能够重现原始训练结果，要么在暗晕功率谱匹配方面表现得更好。主成分的各元素不太可能被赋予物理意义，部分原因在于不同平滑尺度之间的特征高度相关。我们的工作表明，在研究星系偏袒时可能需要包含多个平滑尺度，而这可以通过能够接收高维输入特征空间的机器学习方法轻松实现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
