The Promise of Data Science for the Technosignatures Field

©NSF

Green Bank Observatory

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.

Anamaria Berea, Steve Croft, Daniel Angerhausen
(Submitted on 20 Mar 2019)

Comments: Science white paper submitted in response to the the U.S. National Academies of Science, Engineering, and Medicine's call for community input to the Astro2020 Decadal Survey; 7 pages, 1 figure
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Earth and Planetary Astrophysics (astro-ph.EP); Data Analysis, Statistics and Probability (physics.data-an)
Cite as: arXiv:1903.08381 [astro-ph.IM] (or arXiv:1903.08381v1 [astro-ph.IM] for this version)
Submission history
From: Daniel Angerhausen
[v1] Wed, 20 Mar 2019 08:28:16 UTC (232 KB)
https://arxiv.org/abs/1903.08381
Astrobiology, SETI

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