Proposes that synoptic time domain surveys can probe 10-100 times more Cosmic Haystack volume for technosignatures than traditional radio SETI by searching spatially resolved or multi-star signals over time.
The Promise of Data Science for the Technosignatures Field
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abstract
This paper outlines some of the possible advancements for the technosignatures searches using the new methods currently rapidly developing in computer science, such as machine learning and deep learning. It also showcases a couple of case studies of large research programs where such methods have been already successfully implemented with notable results. We consider that the availability of data from all sky, all the time observations paired with the latest developments in computational capabilities and algorithms currently used in artificial intelligence, including automation, will spur an unprecedented development of the technosignatures search efforts.
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astro-ph.IM 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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SETI in the Spatio-Temporal Survey Domain
Proposes that synoptic time domain surveys can probe 10-100 times more Cosmic Haystack volume for technosignatures than traditional radio SETI by searching spatially resolved or multi-star signals over time.