Artificial intelligence (AI) algorithms trained on real astronomical observations now outperform astronomers in sifting through massive amounts of data to find new exploding stars
Identify new types of galaxies and detect the mergers of massive stars, accelerating the rate of new discovery in the world's oldest science.
But AI, also called machine learning, can reveal something deeper, University of California, Berkeley, astronomers found:
Unsuspected connections hidden in the complex mathematics arising from general relativity—in particular, how that theory is applied to finding new planets around other stars.
In a paper appearing this week in the journal Nature Astronomy, the researchers describe how an AI algorithm developed to more quickly detect exoplanets when such planetary systems pass in front of a background star and briefly brighten it
a process called gravitational microlensing—revealed that the decades-old theories now used to explain these observations are woefully incomplete.
In 1936, Albert Einstein himself used his new theory of general relativity to show how the light from a distant star can be bent by the gravity of a foreground star, not only brightening it as seen from Earth
but often splitting it into several points of light or distorting it into a ring, now called an Einstein ring. This is similar to the way a hand lens can focus and intensify light from the sun.