Model developed using TensorFlow and Keras sifts through data for ‘technosignatures’ from alien worlds
Scientists have developed a machine learning method they think could help filter out interference and more efficiently spot unusual radio signals from space, contributing to the ongoing search for extra-terrestrial intelligence.
Search for extraterrestrial intelligence (SETI) programmes have used radio telescopes for decades to detect unambiguous artificial signals coming from the firmament. However, this search is complicated by interference from human tech, which can generate false positive identifications that are time-consuming to filter out from large data sets.
Research led by Peter Ma, third year physics and mathematics undergraduate at the University of Toronto, used observations from 820 stars, in the form of 115 million snippets of data. The deep learning models the team developed using ML library TensorFlow and Python library Keras, identified around 3 million signals of interest. The group was whittled down to 20,515 interesting signals, which is more than 100 times less than previous analyses of the same dataset, the authors claimed.
