Breakthrough Listen and Kaggle have devised a challenge to sift through radio telescope data for alien signals. Participants are asked to identify anomalous signals artificially inserted in the scans of the targets of the Green Bank Telescope, in order to identify new algorithms for the recognition of extraterrestrial signals
Are we alone in the universe? This has always been one of the deepest and most intriguing questions of mankind. As technology improves, we are finding new and increasingly powerful ways to seek answers. The Breakthrough Listen team at the University of California, Berkeley , is using the world’s most powerful telescopes to scan millions of stars for artificial signs produced by possible alien technology. Now Kaggle – an online community of data scientists and machine learning professionals affiliated with Google Llc – decided to help the team of researchers to interpret the signals collected by these telescopes, sifting them as they once did with gold.
The Breakthrough Listen team is part of the Search for ExtraTerrestrial Intelligence (Seti) and uses the largest orientable dish on the planet, the 100-meter-diameter Green Bank Telescope (Gbt). As in any Seti research, the hope is to be able, sooner or later, to communicate. Humans have built an enormous number of radio devices but, despite this, looking for an alien transmission is like looking for a needle in a huge haystack… a haystack of data collected by modern technology.
Currently, methods of looking for these needles in the haystack use two filters. First, the scans of the targets (the target stars of the survey) are interspersed with scans of other regions of the sky. Reasonably, any signals that appear in both sets of scans do not come from the target star. Second, the data analysis pipeline rejects signals that do not change their frequency, as this most likely means that they are in close proximity to the telescope, as a moving source would have a Doppler shift related to its velocity. These two filters are quite effective, but they can be improved.
Kaggle has devised a competition in which participants are asked to identify anomalous signals in the scans of the Breakthrough Listen targets, in order to identify new algorithms for recognizing extraterrestrial signals. Since there are no confirmed examples of alien signals to be used to train machine learning algorithms, the team has included some simulated signals (called ‘needles’) in the telescope’s data haystack, identifying some so they can be used to train the model. to find others.
The challenge started two weeks ago and there are already 547 registered teams. There is time until July 22 to register – alone or as a team – and until July 29 to propose an algorithm. The one who can identify the most needles will win a cash prize ($ 6,000 for first place, $ 5,000 for second and $ 4,000 for third) and, more importantly, may have the potential to answer one of the biggest questions. of science.
Featured image credit: Green Bank Observatory / National Science Foundation / Breakthrough Listen / University of California, Berkeley SETI Research Center
To know more:
- Go to the website of the initiative ” SETI Breakthrough Listen – ET Signal Search “
Provided by INAF