Announcing a New Plan for Solving the Mystery of Unidentified Aerial Phenomena

Mon, 26 Jul 2021 09:00:00 GMT
Scientific American - Science

The newly organized Galileo Project will use a three-pronged approach to replace unreliable...

One-time events-miracles, for example-do not have scientific credibility.

The nature of credible scientific evidence is particularly critical in the context of unidentified flying objects.

Such a report does not constitute a standard scientific measurement in a reproducible setup.

We must humbly recognize that a complete quantitative knowledge of the conditions in an experimental setup is a fundamental prerequisite for scientific data to be credible.

With this principle in mind, the Pentagon report that was delivered to Congress on June 25, 2021 is intriguing enough to motivate scientific inquiry towards the goal of identifying its unidentified aerial phenomena.

This is the rationale for the new Galileo Project that I initiated recently to scientifically explore the nature of UAP. The primary objective of this research endeavor is to bring the search for extraterrestrial technological signatures of extraterrestrial technological civilizations from accidental or anecdotal observations to the mainstream of transparent, validated and systematic scientific research.

Extensive artificial intelligence/deep learning and algorithmic approaches are needed to differentiate atmospheric phenomena from birds, balloons, commercial aircraft or drones, and from potential technological objects of terrestrial or other origin surveying our planet, such as satellites.

For the purpose of high-contrast imaging, each telescope will be part of a detector array of complementary capabilities from radar systems to optical and infrared cameras on telescopes.

The research team will conceptualize and design a launch-ready space mission to image unusual interstellar objects such as 'Oumuamua by intercepting their trajectories on their approach to the sun or by using ground-based survey telescopes to discover interstellar meteors.

As is the case for AI/DL systems that learn from interactions with their environment, the scientific process of collecting evidence is key for a reliable revision of our own view of the world around us.

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