Predicting future astronomical events using deep learning

TL;DR

This paper introduces a deep learning model capable of predicting future astrophysical events, such as galaxy mergers and gravitational lens evolution, while maintaining spatial-temporal coherence, and proposes a new accuracy metric called Correctness Factor.

Contribution

The work presents a novel deep learning approach for astrophysical event prediction and introduces the Correctness Factor metric for evaluating prediction accuracy.

Findings

Successfully predicted galaxy merger evolutions.

Predicted gravitational lensing events with high accuracy.

Introduced a new metric for prediction correctness.

Abstract

In a quest towards an intelligent decision-making machine, the ability to make plausible predictions is the central pillar of its intelligence. A predicting algorithm's central idea is to understand the governing physical rules and make plausible and apt predictions based on the same governing laws. Extending the study towards the astrophysical phenomenon puts the model's ability to test since the model has to understand various parameters that govern the dynamics of the event and understand the spatial and temporal evolution by applying the plausible laws. This work presents a deep learning model to predict plausible future events that maintain spatial and temporal coherence. We have trained over two broad classes, the evolution of Sa, Sb, S0, and Sd galaxy mergers and evolution of gravitational lenses with a higher redshift of the foreground galaxy having $15M_{\odot}$. We extended our work towards developing a direct measure of the performance metric for any prediction algorithm. We thereby introduce a novel metric, Correctness Factor (CF), which directly outputs how accurate a prediction is.