论文标题
宇宙风筝：自动编码宇宙微波背景
Cosmic Kite: Auto-encoding the Cosmic Microwave Background
论文作者
论文摘要
在这项工作中，我们通过自动编码器介绍了宇宙微波背景TT功率谱的研究结果，其中潜在变量是宇宙学参数。使用由CAMB代码数值计算的随机宇宙学组成的数据集对该方法进行了训练和校准。由于自动编码器的特定体系结构，编码器部分是一个模型，该模型可估算给定功率谱的最大样本参数。另一方面，解码器部分是一个模型，该模型可以从宇宙学参数中计算功率谱，并且可以在完全贝叶斯分析中用作正向模型。我们表明，编码器能够估算出真正的宇宙学参数，精度从$ \ \ \％\％$ $ \％$ \ $ \ of 0.2 \％$（取决于宇宙学参数），而解码器则以$ \ of $ \ y of your y y 0.0018 \％$ $ $ $ $ $ $ $的平均百分比计算功率谱。我们还证明，解码器一一改变宇宙学参数时恢复了预期的趋势，并且通过贝叶斯分析对宇宙学参数的估计并没有引入任何显着偏见。这些研究提供了公开可用的宇宙风筝python软件，可以从https://github.com/martindelosrios/cosmic-kite下载和安装。尽管与传统方法相比，该算法并不能提高测量值的精度，但它大大减少了计算时间，这代表了强迫潜在变量具有物理解释的首次尝试。
In this work we present the results of the study of the cosmic microwave background TT power spectrum through auto-encoders in which the latent variables are the cosmological parameters. This method was trained and calibrated using a data-set composed by 80000 power spectra from random cosmologies computed numerically with the CAMB code. Due to the specific architecture of the auto-encoder, the encoder part is a model that estimates the maximum-likelihood parameters from a given power spectrum. On the other hand, the decoder part is a model that computes the power spectrum from the cosmological parameters and can be used as a forward model in a fully Bayesian analysis. We show that the encoder is able to estimate the true cosmological parameters with a precision varying from $\approx 0.004\% $ to $\approx 0.2\% $ (depending on the cosmological parameter), while the decoder computes the power spectra with a mean percentage error of $\approx 0.0018\% $ for all the multipole range. We also demonstrate that the decoder recovers the expected trends when varying the cosmological parameters one by one, and that it does not introduce any significant bias on the estimation of cosmological parameters through a Bayesian analysis. These studies gave place to the Cosmic Kite python software that is publicly available and can be downloaded and installed from https://github.com/Martindelosrios/cosmic-kite. Although this algorithm does not improve the precision of the measurements compared with the traditional methods, it reduces significantly the computation time and represents the first attempt towards forcing the latent variables to have a physical interpretation.
