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Indian astronomers use AI to search for Earth-like planets
Indian astronomers use AI to search for Earth-like planets

Indian astronomers use AI to search for Earth-like planets

Multi-Stage Memetic Binary Tree Anomaly Identifier is based on a novel multi-stage memetic algorithm.

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Key takeaways

  • Indian astronomers have developed an AI tool to identify potentially habitable planets.
  • The tool, based on a novel algorithm, aims to enhance the detection of life-supporting planets.
  • Currently, there are 60 identified potentially habitable planets among thousands of confirmed candidates.
  • The tool reduces the time and costs associated with studying exoplanets using expensive telescopes.
  • The algorithm employs a unique approach to detect anomalies in planetary data for efficient screening.
5 min

Astronomers at the Indian Institute of Astrophysics and BITS Pilani, Goa, have developed an AI-powered tool to find habitable planets with a high probability of hosting life. 

There are 60 potentially habitable planets out of about 5,00 confirmed and 8,000 proposed planets capable of harbouring life, said the study. Researchers have developed an AI-based algorithm to detect anomalies and extended it to an unsupervised clustering algorithm to use it to identify the probably habitable exoplanets from the exoplanet datasets to reduce the time needed to study these planets and save overheads incurred by deploying expensive telescopes.

Multi-Stage Memetic Binary Tree Anomaly Identifier (MSMBTAI) is based on a novel multi-stage memetic algorithm (MSMA). MSMA uses the generic notion of a meme, which is an idea or knowledge that gets transferred from one person to another by imitation. A meme indicates cross-cultural evolution in posterity and, therefore, can induce new learning mechanisms as generations pass. The algorithm can act as a quick screening tool for evaluating habitability perspectives from observed properties.

“Earth being the only habitable planet among thousands of planets is defined as an anomaly. We explored whether similar anomaly candidates can be found using novel anomaly detection methods,” said the researchers.

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