mlsanityData to 7 Oct 2026

DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data

arXiv 2205.00701 ↗First posted 2 May 2022astro-ph.IM astro-ph.CO cs.AIcited by 2

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Abstract (arXiv)

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 ≈ 3% to ≈ 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 128 references that are in the map. First posted on arXiv 2 May 2022.

Builds on

17 of 128 references.

References · 17

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5 more
  1. 2021DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learningcited by 7
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Time Series Analysis in Compressor-Based Machines: A Surveycited by 0
  2. 2026DeepRed: an architecture for redshift estimationcited by 0