Data compression in cosmology: A compressed likelihood for Planck data
Data compression in cosmology: A compressed likelihood for
Planck
data
复制标题
宇宙学中的数据压缩：普朗克数据的压缩可能性
作者：
H. Prince;J. Dunkley
我们将大规模优化参数估计和数据压缩技术（mped）应用于公共Planck 2015温度似然，将数据空间的维度降低到每个感兴趣参数一个数字。我们介绍了cosmop，一个用Python实现的轻量级且方便的压缩似然代码。通过这样做，我们表明$\ell<30$普朗克温度似然可以很好地近似于两个高斯分布数据点，这允许我们用简单的高斯似然取代基于地图的$\ell$低温似然。我们提供了Planck 2015 Plik＿lite温度可能性的Python实现，其中包括这些低$\ell$ bin温度数据（Planck-lite-py）。我们没有明确地使用大尺度偏振数据，而是对这些数据得出的再电离的光学深度施加了一个先验条件。我们证明了用cosmop恢复的$\Lambda$ CDM参数与未压缩的似然值在0.1 $\sigma$以内是一致的，并且测试了一个7参数扩展模型的表现也很好。
We apply the massively optimized parameter estimation and data compression technique (MOPED) to the public Planck 2015 temperature likelihood, reducing the dimensions of the data space to one number per parameter of interest. We present CosMOPED, a lightweight and convenient compressed likelihood code implemented in Python. In doing so we show that the $\ell<30$ Planck temperature likelihood can be well approximated by two Gaussian distributed data points, which allows us to replace the map-based low-$\ell$ temperature likelihood by a simple Gaussian likelihood. We make available a Python implementation of Planck's 2015 Plik_lite temperature likelihood that includes these low-$\ell$ binned temperature data (Planck-lite-py). We do not explicitly use the large-scale polarization data in CosMOPED, instead imposing a prior on the optical depth to reionization derived from these data. We show that the $\Lambda$CDM parameters recovered with CosMOPED are consistent with the uncompressed likelihood to within 0.1$\sigma$, and test that a 7-parameter extended model performs similarly well.