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Los Angeles. Researchers recently used ‘deep learning’ techniques to study a dataset of near-Earth stars and uncovered eight unknown signals. The study said the team of researchers was led by Peter Ma, a graduate student at the University of Toronto. The team also included scientists from the SETI Institute, Breakthrough Listen and scientific research institutes around the world. This study has been published in the research journal ‘Nature Astronomy’.
The question that often arises when considering the possibility of discovering technologically advanced extraterrestrial life is, ‘If they’re out there, why haven’t we discovered them yet?’, says the Artificial Intelligence (AI)-based study. The response is that we have discovered only a small part of the Milky Way. Furthermore, algorithms developed decades ago for early digital computers may be outdated and inefficient when applied to modern petabyte-scale datasets.
Scientists got many hints
In total, we searched through 150 TB of data from 820 nearby stars on a dataset that was discovered in 2017 by older techniques, but interesting signals were not detected, said Peter Ma, lead author of the study. Go on.’ He said that we are now expanding this search effort to Meerkat telescope and beyond to one million stars. We believe this type of work will help in the effort to answer the question ‘Are we alone in the universe?’
The study says that the most common technique is to search for radio signals, because the waves provide information about the incredible distances between stars. It states that radio signals travel rapidly through the dust and gas permeating space and do so at the speed of light, which is about 20,000 times faster than our best rockets. Ma’s research assistant and astronomer at the SETI Institute and the French National Center for Scientific Research said, “The application of these techniques on a large scale will be transformative for radiotechnological science.”
