New AI tool that detects star flares could help us find habitable Planets

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A new AI system that detects flares erupting from stars could help astronomers find habitable planets, according to the tool’s inventors.

Detecting Stellar Flares with Neural Networks

The neural network identifies the characteristic light patterns of stellar flares — intense bursts that can strip away the atmospheres of nearby planets. By mapping the frequency and location of these events, astronomers can better pinpoint the most promising regions for habitable worlds.

Traditionally, researchers detect flares through a laborious manual review of changes in stellar brightness over time. The new AI tool accelerates this process, delivering faster and more reliable results.

Training and Results from the 2026 Analysis

The researchers trained the neural network on a labeled dataset of confirmed flares and non-flares. When applied to observations of more than 3,200 stars, the model identified over 23,000 flares across tens of thousands of young stars.

The study revealed that Sun-like stars produce only a few flares, with activity declining sharply after roughly 50 million years. In 2026, updated runs of the same pipeline on expanded datasets confirmed these trends, reinforcing the link between stellar age and flare rates.

“That is excellent for fostering planetary atmospheres — more moderate stellar environments mean the atmospheres have a better prospect of surviving,” said Adina Feinstein, a University of Chicago graduate student and first author on the paper.

Implications for Red Dwarfs and Exoplanet Habitability

Cooler stars known as red dwarfs, however, exhibited far more frequent flaring, which could hinder planets from retaining stable atmospheres.

The team plans to refine the neural network further so it can search for planets orbiting young stars directly.

“This will lead to a ‘growth of the machines’ where we could use machine learning algorithms to discover a lot of exciting new planets with the same procedures,” said UNSW Sydney’s Dr Ben Montet, co-author of the study.

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