Machine-learning techniques that mimic human recognition and dreaming processes are being deployed in the search for habitable worlds beyond our solar system. A deep belief neural network, called RobERt (Robotic Exoplanet Recognition), has been developed by astronomers at UCL to sift through detections of light emanating from distant planetary systems and retrieve spectral information about the gases present in the exoplanet atmospheres.
RobERt will be presented at the National Astronomy Meeting (NAM) 2016 in Nottingham by Dr Ingo Waldmann on Tuesday 28th June.
“Different types of molecules absorb and emit light at specific wavelengths, embedding a unique pattern of lines within the electromagnetic spectrum,” explained Dr Waldmann, who leads RobERt’s development team. “We can take light that has been filtered through an exoplanet’s atmosphere or reflected from its cloud-tops, split it like a rainbow and then pick out the ‘fingerprint’ of features associated with the different molecules or gases. Human brains are really good at finding these patterns in spectra and label them from experience, but it’s a really time consuming job and there will be huge amounts of data.
We built RobERt to independently learn from examples and to build on his own experiences. This way, like a seasoned astronomer or a detective, RobERt has a pretty good feeling for what molecules are inside a spectrum and which are the most promising data for more detailed analysis. But what usually takes days or weeks takes RobERt mere seconds.”