How SETI Uses Machine Learning for Analyzing Radio Signals

Authors

  • Aakaash A Department of Computer Science, Vellore Institute of Technology, Kelambakkam - Vandalur Rd, Rajan Nagar, Chennai, Tamil Nadu 600127

DOI:

https://doi.org/10.61359/11.2106-2604

Keywords:

SETI, Machine Learning, Radio Signals

Abstract

There are more stars in the universe than the number of grains of sand on Earth. And orbiting each of these are more worlds than we could ever visit, more chances than we could ever count. And yet, we have heard nothing - no messages, no signals, no signs. Just absolute silence, stretching across billions of years and light years alike. But as Carl Sagan himself says, "The absence of evidence is not evidence of absence". Maybe they don't know we're here. Maybe they're too far to reach us. Maybe they're choosing not to speak. Or even wilder - they've been speaking all along, and we haven't known how to listen. So, we built ears as wide as continents and tuned our them to the language of numbers - with structured signals, repeating patterns, the rhythms hidden in randomness, the fingerprints of thought etched in the static. And this brings us to SETI, the Search for Extraterrestrial Intelligence.

Downloads

Download data is not yet available.

References

Downloads

Published

2026-03-30

How to Cite

How SETI Uses Machine Learning for Analyzing Radio Signals. (2026). Acceleron Aerospace Journal, 6(1), 1677-1681. https://doi.org/10.61359/11.2106-2604

Similar Articles

1-10 of 33

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)