Vast data sets and machine learning could link SETI to quantum computing

Vast data sets and machine learning could link SETI to quantum computing

Caleb A. Scharf

Radio transmissions might exist across thousands if not millions of discrete frequencies. Or in other electromagnetic radiation at visible, infrared, ultraviolet, even gamma-ray frequencies. All capable of being pulsed, modulated, polarized and varied across time and according to unknown strategies or accidents. There could be physical structures obscuring otherwise natural starlight, or glowing with thermal energy. Or deliberate neutrino emissions, gravitational waves, engineered molecular vibrational emissions, and isotopically encoded messages. The list is enormous. 

Right now we've barely scratched the surface. Even quantifying our shortcomings is quite challenging. In 2018 a paper by Wright, Kanodia, and Lubar applied a multi-dimensional measure to radio searches and concluded that, in colloquial terms, we've thus far "looked" at the equivalent of a hot-tub's worth of water out of all of Earth's oceans.

But the game is afoot to improve on this. Recently the Breakthrough Listen project released a new dataset of some 2 Petabytes of radio telescope data (from the Green Bank and Parkes observatories). That data consists of a series of measurements around the plane of the Milky Way (including 20 known exoplanetary systems that might witness Earth transiting the Sun), the galactic center, and the interstellar comet 21/Borisov.

Yet, as impressive as this raw data is, it too represents just a droplet of what's needed to begin to truly constrain the odds of technologically visible life in our neighborhood. And that raises the question of how we are going to be able to crunch through SETI data as it gets bigger and bigger in scope.