AI Spots Rare Supernova SN 2023zkd in Real-Time Detection
In a groundbreaking development, artificial intelligence (AI) has played a pivotal role in the field of astronomy, helping astronomers unravel the mysteries of the universe. One such instance is the discovery of Supernova SN 2023zkd, a unique event that showcased the potential of AI in astronomical research.
The supernova, discovered with the aid of the AI system LAISS developed by the University of Tokyo, displayed two comparable brightness peaks separated by 240 days, or about eight months. This unusual behaviour is rare for supernovae, as most do not show such extended emissions before the explosion. The precursor emissions of SN 2023zkd were observed for over four years, a phenomenon that is unusual for supernovae.
The spectroscopic analysis of SN 2023zkd revealed strongly asymmetric, multicomponent Balmer and He I profiles, suggesting a catastrophic encounter between a massive star and a black hole companion. The companion either swallowed the star partially or tore it apart before it could explode on its own, resulting in the observed explosion light being the result of the material the star had shed in the years before it died.
AI was also instrumental in assessing data about black holes. An international team of astronomers used AI to help them understand the behaviour of black holes, particularly the central black hole of the Milky Way. The AI analysis revealed that the central black hole of our galaxy is revolving at nearly its maximum speed.
The Rubin Observatory, with its real-time AI detection, will enable astronomers to discover and study rarer, complex events like SN 2023zkd. The observatory, with its Legacy Survey for Space and Time, is expected to discover ∼105 yr−1 of SNe IIn, increasing the photometric samples of such events. Algorithms designed to flag these long-lived and re-brightening transients will play a critical role in characterizing the full breadth of strongly interacting events.
The latest study also saw the use of AI in analysing data from the Event Horizon Telescope (EHT) Collaboration, which presented images of black hole Sagittarius A*. A Bayesian neural network was fed millions of files to make a more accurate comparison between the EHT data and the models. This AI network was trained to uncover information from the data behind the images, aiding the understanding of black holes.
AI is not limited to astronomy. It is extensively used across various industries, including finance, healthcare, manufacturing, cybersecurity, disaster management, and customer service. Companies like Northrop Grumman Corporation, a key contractor for space missions including the James Webb Space Telescope, are expanding their use of AI in space to improve spacecraft operations.
In this new era of AI-driven astronomy, the possibilities are endless. As we continue to harness the power of AI, we can expect to uncover more mysteries of the universe and push the boundaries of our understanding.
