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使用卷积神经网络进行强引力透镜的快速自动化分析

Yashar D Hezaveh^1,2, Laurence Perreault Levasseur^1,2, Philip J Marshall^1,2

¹Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, California, USA.

|September 1, 2017

概括

深度卷积神经网络快速估计引力透镜参数,克服目前使用的缓慢复杂方法. 这一突破使得宇宙学研究中的大型天文数据集能够更快地进行分析.

科学领域:

天体物理学
宇宙学
机器学习

背景情况:

强大的引力透镜量化了宇宙结构和物质分布.
目前的分析方法 (最大概率建模) 在计算上昂贵且耗时.
未来的天空调查将发现大量的引力透镜.

研究的目的:

开发一个快速和自动化的方法来估计引力透镜参数.
为了规避传统的最大概率模型的局限性.
为了使大重力透镜数据集的有效分析.

主要方法:

使用深度卷积神经网络 (CNN) 来进行参数估计.
采用独立组件分析来自动删除镜片的光.
训练有素的CNN可以恢复异热圆密度的参数.

主要成果:

它们的准确性与先进的模型相美.
这种新方法比传统方法快大约1000万倍.
在单个GPU上大约1秒内分析100个系统.

结论:

深度学习为引力透镜分析提供了高效和自动化的解决方案.
这种方法使得非专家能够获得透镜参数估计.
这种方法对于处理未来天文调查所预期的大量数据至关重要.

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