论文标题
用宇宙计时仪测量哈勃常数：一种机器学习方法
Measuring the Hubble Constant with cosmic chronometers: a machine learning approach
论文作者
论文摘要
基于CEPHEID的Hubble常数（$ H_0 $）的本地测量值IA类型IA Supernova的$ \ \ \ \5σ$与Planck CMB观测值的估计值$ \5σ$不同。为了更好地理解这种$ H_0 $的张力，不同分析方法的比较将是基础，以解释下一代调查提供的数据集。在本文中，我们通过对膨胀率的合成数据进行回归分析来测量机器学习算法，假设红移和不同级别的不确定性级别。我们将不同的回归算法的性能作为额外树，人工神经网络，梯度增强，支持向量机器的性能进行比较，我们发现，在大多数情况下，支持向量机在偏见差异方面表现出最佳性能，在大多数情况下表现出对非耐心的回归方法的竞争性交叉检查，例如竞争性的回归方法。
Local measurements of the Hubble constant ($H_0$) based on Cepheids e Type Ia supernova differ by $\approx 5 σ$ from the estimated value of $H_0$ from Planck CMB observations under $Λ$CDM assumptions. In order to better understand this $H_0$ tension, the comparison of different methods of analysis will be fundamental to interpret the data sets provided by the next generation of surveys. In this paper, we deploy machine learning algorithms to measure the $H_0$ through a regression analysis on synthetic data of the expansion rate assuming different values of redshift and different levels of uncertainty. We compare the performance of different regression algorithms as Extra-Trees, Artificial Neural Network, Gradient Boosting, Support Vector Machines, and we find that the Support Vector Machine exhibits the best performance in terms of bias-variance tradeoff in most cases, showing itself a competitive cross-check to non-supervised regression methods such as Gaussian Processes.
