The Dreaming AI Hunting For Exoplanet Atmospheres

Humans are better than computers at looking for patterns in space spectra, so one researcher has taught a neural network how to think like us to find anomalies

-  Dr Ingo Waldmann

EXAMINING SPACEISN’T easy – there’s an awful lot of it. That’s why Dr. Ingo Waldmann, a post-doctoral research associate at University College London, turned to computers for help in identifying the make-up of atmosphere around exoplanets. But what’s relatively easy for humans to do is difficult for machines: we’re simply better at pattern recognition than an unthinking algorithm could ever be. The issue is that it takes time to do it.

For help, Dr. Waldmann turned to machine learning, creating software called Robotic Exoplanet Recognition (RobERt, for short), which uses a neural network – artificial intelligence modelled on human brains. Dr. Waldmann then trained RobERt with a database of 80,000 pieces of data to identify water, methane and other key spectra in the atmosphere around exoplanets.

RobERt boasts a 99.7% accuracy rate, but it can also “dream”: ask the robot to “imagine” water, and it approximates what that spectrum would look like based on its own experience. We spoke to Dr Waldmann to find out why neural networks are the right technology to eyeball space, and to find out what else RobERt might discover.