Fast bayesian inference for slow-roll inflation
Fast bayesian inference for slow-roll inflation
复制标题
慢速通货膨胀的快速贝叶斯推理
DOI：
10.1093/mnras/stu109
复制
发表时间：
2013
影响因子：
4.8
通讯作者：
C. Ringeval
中科院分区：
文献类型：
--
作者：
C. Ringeval
我们提出并讨论了一种新方法，可以在慢滚动通货膨胀的框架内将执行贝叶斯推理和参数估计的速度提高几个数量级。该方法依赖于对暴胀有效可能性的确定，该可能性是标量扰动的原始振幅的函数，并辅以必要数量的所谓哈勃流函数以达到所需的精度。从任何宇宙学数据集开始，有效可能性是通过对标准宇宙学参数的边缘化获得的，从早期宇宙的角度来看，这里被视为“麻烦”。由于是低维的，因此可以训练基本的机器学习算法来准确地再现其多维形状，然后用作对通货膨胀模型执行快速贝叶斯推理的代理。使用普朗克宇宙微波背景数据对暴胀大场模型进行原始参数估计，说明了该方法的鲁棒性和准确性。特别是，在所有可能的再加热历史中被边缘化，我们发现在 95% 的置信度下验证 p < 2.3 的潜力的功率指数。 © 2014 作者版权所有，由牛津大学出版社代表英国皇家天文学会出版。
We present and discuss a new approach increasing by orders of magnitude the speed of performing Bayesian inference and parameter estimation within the framework of slow-roll inflation. The method relies on the determination of an effective likelihood for inflation which is a function of the primordial amplitude of the scalar perturbations complemented with the necessary number of the so-called Hubble flowfunctions to reach the desired accuracy. Starting from any cosmological data set, the effective likelihood is obtained by marginalization over the standard cosmological parameters, here viewed as 'nuisance' from the early Universe point of view. As being low dimensional, basic machine-learning algorithms can be trained to accurately reproduce its multidimensional shape and then be used as a proxy to perform fast Bayesian inference on the inflationary models. The robustness and accuracy of the method are illustrated using the Planck cosmic microwave background data to perform primordial parameter estimation for the large field models of inflation. In particular, marginalized over all possible reheating history, we find the power index of the potential to verify p < 2.3 at 95 per cent of confidence. © 2014 The AuthorsPublished by Oxford University Press on behalf of the Royal Astronomical Society.