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 $\approx$ 19% to $\approx$ 43%, 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.

翻译：引力透镜是由大质量天体产生的相对论效应，这种效应会弯曲其周围的时空。这是天体物理学中一个深入研究的课题，既能验证理论相对论结果，又能研究其他方式下不可见的微弱天体。近年来，机器学习方法被应用于支持引力透镜现象的分析，通过检测由亮度变化时间序列相关的图像组成的数据集中的透镜效应。然而，现有方法要么仅考虑图像而忽略时间序列数据，要么在最困难的数据集上准确率相对较低。本文提出了DeepGraviLens，一种新颖的多模态网络，用于对属于一个非透镜系统类型和三个透镜系统类型的时空数据进行分类。根据所考虑的数据集，它比当前最先进的准确率结果提高了约19%至约43%。这一改进将加速未来天体物理巡天中透镜天体的分析，这些巡天将利用来自例如维拉·鲁宾天文台收集的PB级数据。