DeepGraviLens：一种用于对引力透镜数据进行分类的多模态架构
DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data
AI总结：
本文提出多模态网络DeepGraviLens，分类引力透镜时空数据，超越现有方法约3%-11%准确率，加速巡天数据分析。
摘要：
引力透镜是大质量天体产生的相对论效应，这些天体弯曲了它们周围的时空。这是天体物理学中一个被深入研究的课题，它使得验证理论相对论结果以及研究那些否则将不可见的微弱天体成为可能。近年来，机器学习方法已被应用于支持引力透镜现象的分析，通过检测由与亮度变化时间序列相关联的图像组成的数据集中的透镜效应。然而，最先进的方法要么只考虑图像而忽略时间序列数据，要么在最困难的数据集上实现相对较低的准确率。本文介绍了DeepGraviLens，一种新颖的多模态网络，用于对属于一种非透镜系统类型和三种透镜系统类型的时空数据进行分类。根据所考虑的数据集，它超越了当前最先进的准确率结果约3%至约11%。这一改进将有助于加速即将进行的天体物理巡天中对透镜天体的分析，这些巡天将利用收集到的PB级数据，例如来自维拉·C·鲁宾天文台的数据。
英文摘要：
Gravitational lensing is the relativistic effect generated by massive bodies, which bend the space-time surrounding them. It is a deeply investigated topic in astrophysics and allows validating theoretical relativistic results and studying faint astrophysical objects that would not be visible otherwise. In recent years Machine Learning methods have been applied to support the analysis of the gravitational lensing phenomena by detecting lensing effects in data sets consisting of images associated with brightness variation time series. However, the state-of-art approaches either consider only images and neglect time-series data or achieve relatively low accuracy on the most difficult data sets. This paper introduces DeepGraviLens, a novel multi-modal network that classifies spatio-temporal data belonging to one non-lensed system type and three lensed system types. It surpasses the current state of the art accuracy results by $\approx 3\%$ to $\approx 11\%$, depending on the considered data set. Such an improvement will enable the acceleration of the analysis of lensed objects in upcoming astrophysical surveys, which will exploit the petabytes of data collected, e.g., from the Vera C. Rubin Observatory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
