Multimodal multi-output ordinal regression for discovering gravitationally-lensed transients

Abstract

Gravitational lenses are caused by massive astronomical objects that distort space-time, bending light. They can distort transient astrophysical events, such as supernovae (SN), which are the subject of extensive study. However, gravitationally-lensed supernovae are rare, with only a few detected so far. Future astronomical surveys will collect huge amounts of data, calling for automated and accurate discovery techniques to find them. Still, only a few works aim to discover gravitationally-lensed supernovae, most use only a few classes to characterize candidate observations, and only a few exploit spatial and temporal information. This work introduces Hydra, a novel pipeline designed to process spatio-temporal data for identifying and counting astronomical objects, including gravitational lenses and transients. Hydra performs two tasks: (i) counting the occurrences of 7 types of astronomical objects within each observation and (ii) classifying candidate events and objects (e.g. gravitational lenses and transient events). Across four datasets, Hydra achieves an average macro F _1 score higher than 79% for the counting task and macro F _1 scores ranging from ${\approx}59\%$ to ${\approx}94\%$ for classification. These results demonstrate its potential for improving automated discovery in future astronomical surveys and for counting objects in multimodal data.

Keywords

• multimodal
• ordinal regression
• gravitational lens
• ensembling
• transient