Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
arxiv(2024)
摘要
Strong gravitational lensing can be used as a tool for constraining the
substructure in the mass distribution of galaxies. In this study we investigate
the power spectrum of dark matter perturbations in a population of 23 Hubble
Space Telescope images of strong galaxy-galaxy lenses selected from The Sloan
Lens ACS (SLACS) survey. We model the dark matter substructure as a Gaussian
Random Field perturbation on a smooth lens mass potential, characterized by
power-law statistics. We expand upon the previously developed machine learning
framework to predict the power-law statistics by using a convolutional neural
network (CNN) that accounts for both epistemic and aleatoric uncertainties. For
the training sets, we use the smooth lens mass potentials and reconstructed
source galaxies that have been previously modelled through traditional fits of
analytical and shapelet profiles as a starting point. We train three CNNs with
different training set: the first using standard data augmentation on the
best-fitting reconstructed sources, the second using different reconstructed
sources spaced throughout the posterior distribution, and the third using a
combination of the two data sets. We apply the trained CNNs to the SLACS data
and find agreement in their predictions. Our results suggest a significant
substructure perturbation favoring a high frequency power spectrum across our
lens population.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要