论文标题
宇宙学的光度红移：使用贝叶斯神经网络提高准确性和不确定性估计
Photometric Redshifts for Cosmology: Improving Accuracy and Uncertainty Estimates Using Bayesian Neural Networks
论文作者
论文摘要
我们提出了结果，探讨了概率深度学习模型可以通过估计从光度法到星系（红移）的距离，从大规模天文学调查中发挥的作用。由于来自这些新的和即将进行的天空调查的大量数据，使用星系光度法的机器学习技术被越来越多地用于预测银河红移，这对于推断宇宙学参数（例如暗能量的本质）很重要。相关的不确定性估计也是关键的测量，但是，通用的机器学习方法通常仅提供点估计，并且缺乏不确定性信息作为输出。我们将贝叶斯神经网络（BNN）作为一种有希望的方法来提供红移值的准确预测。我们已经从Hyper Suprime-Cam调查中编辑了一个新的Galaxy培训数据集，该数据集旨在模仿大型调查，但在较小的天空中。我们使用机器学习，天文学和概率指标来评估光度法的光度红移（Photo-Z）预测的性能和准确性。我们发现，虽然贝叶斯神经网络的性能不如仅通过点估计估计的照片-Z值进行评估，但BNN可以提供宇宙学所需的不确定性估计值
We present results exploring the role that probabilistic deep learning models can play in cosmology from large scale astronomical surveys through estimating the distances to galaxies (redshifts) from photometry. Due to the massive scale of data coming from these new and upcoming sky surveys, machine learning techniques using galaxy photometry are increasingly adopted to predict galactic redshifts which are important for inferring cosmological parameters such as the nature of dark energy. Associated uncertainty estimates are also critical measurements, however, common machine learning methods typically provide only point estimates and lack uncertainty information as outputs. We turn to Bayesian neural networks (BNNs) as a promising way to provide accurate predictions of redshift values. We have compiled a new galaxy training dataset from the Hyper Suprime-Cam Survey, designed to mimic large surveys, but over a smaller portion of the sky. We evaluate the performance and accuracy of photometric redshift (photo-z) predictions from photometry using machine learning, astronomical and probabilistic metrics. We find that while the Bayesian neural network did not perform as well as non-Bayesian neural networks if evaluated solely by point estimate photo-z values, BNNs can provide uncertainty estimates that are necessary for cosmology
