Artificial intelligence Is Outperforming Astronomers to find Earth like Planets

AI is good at Math

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A.I. Boost Search for Exoplanets

I really find how A.I. is improving Astronomy fascinating as A.I. is good at detecting things in data humans wouldn’t be able to recognize.

In 2022, it’s official, Artificial intelligence (AI) systems trained on real astronomical observations now surpass astronomers in filtering through massive amounts of data to find new exploding stars, identify new types of galaxies, and detect the mergers of massive stars, boosting the rate of new discovery in the world’s oldest science.

We are learning more about our Galaxy thanks to advances in artificial intelligence. Astronomers are now becoming Human-AI hybrid academics.

A.I. is Revealing the Unsuspected Maths Behind Spotting Exoplanets ideal for Life

• 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 last 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.

It’s not surprising then that A.I. is facilitating how we might spot the planet that will become a multi-planetary civilization outside of our home system if we are able to survive long enough to develop the ability to get there. As a futurist, I can say that this remains highly uncertain.

A.I. is able to go through the data with an understanding of Einstein’s general relativity that 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.

The AI algorithm, however, pointed to a mathematical way to unify the two major kinds of degeneracy in interpreting what telescopes detect during microlensing, showing that the two "theories" are really special cases of a broader theory that the researchers admit is likely still incomplete.

As our telescopes get better and are brought to work, our A.I. will also be more sophisticated in augmenting the work of Astronomers.

Josh Bloom said:

"A machine learning inference algorithm we previously developed led us to discover something new and fundamental about the equations that govern the general relativistic effect of light- bending by two massive bodies," Joshua Bloom wrote in a blog post last year when he uploaded the paper to a preprint server, arXiv. Bloom is a UC Berkeley professor of astronomy and chair of the department.

A.I. Maths and Pattern Recognition

He compared the discovery by UC Berkeley graduate student Keming Zhang to connections that Google's AI team, DeepMind, recently made between two different areas of mathematics. Taken together, these examples show that AI systems can reveal fundamental associations that humans miss.

In some ways machine learning can thus extend human perception beyond normal ranges in tasks such as spotting Earth-like exoplanets, just as we are seeing it being able to do in medical diagnosis, early detection of diseases and noticing other things that we don’t even know how it connects the dots.

Why is Changes the Future of Astronomy Forever

• The first time A.I. overtakes human Astronomers

• A.I. may impact the future of science and research at scale

"I argue that they constitute one of the first—if not the first—time[s] that AI has been used to directly yield new theoretical insight in math and astronomy," Bloom said. "Just as Steve Jobs suggested computers could be the bicycles of the mind, we've been seeking an AI framework to serve as an intellectual rocket ship for scientists."

A Milestone in A.I.’s Impact on Scientific Discovery

"This is kind of a milestone in AI and machine learning," emphasized co-author Scott Gaudi, a professor of astronomy at The Ohio State University and one of the pioneers of using gravitational microlensing to discover exoplanets. "Keming's machine learning algorithm uncovered this degeneracy that had been missed by experts in the field toiling with data for decades. This is suggestive of how research is going to go in the future when it is aided by machine learning, which is really exciting."

A.I. Will Take Over Microlensing Expertise

Why does it matter?

Microlensing is a form of gravitational lensing in which the light from a background source is bent by the gravitational field of a foreground lens to create distorted, multiple and/or brightened images.

Discovering exoplanets with microlensing.

Seen from Earth (left), a planetary system moving in front of a background star (source, right) distorts the light from that star, making it brighten as much as 10 or 100 times. Because both the star and exoplanet in the system bend the light from the background star, the masses and orbital parameters of the system can be ambiguous. An AI algorithm developed by UC Berkeley astronomers got around that problem, but it also pointed out errors in how astronomers have been interpreting the mathematics of gravitational microlensing. Credit: Diagram courtesy of Research Gate

More than 5,000 exoplanets, or extrasolar planets, have been discovered around stars in the Milky Way, though few have actually been seen through a telescope—they are too dim.

The James Webb Space Telescope might be a game changer. Gleaming in shades of gold, silver, and crinkled lavender, the $10-billion instrument was too big to fit inside one of the world’s biggest rockets, they had to fold it.

Most of these 5,000 exoplanets have been detected because they create a Doppler wobble in the motions of their host stars or because they slightly dim the light from the host star when they cross in front of it—transits that were the focus of NASA's Kepler mission. Only a few more than 100 have been discovered by a third technique, microlensing. But now with this A.I., that could change radically.

The paper was published May 23, 2022, in the journal Nature Astronomy

• One of the main goals of NASA's Nancy Grace Roman Space Telescope, scheduled to launch by 2027, is to discover thousands more exoplanets via microlensing

• By the 2030s we’ll likely knows tens of thousands more good Exoplanet candidates for the search for life and for potential human colonization.

• The technique has an advantage over the Doppler and transit techniques in that it can detect lower-mass planets, including those the size of Earth, that are far from their stars, at a distance equivalent to that of Jupiter or Saturn in our solar system.

A.I. will Get Better at Microlensing

Bloom, Zhang and their colleagues set out two years ago to develop an AI algorithm to analyze microlensing data faster to determine the stellar and planetary masses of these planetary systems and the distances the planets are orbiting from their stars.

Such an algorithm would speed analysis of the likely hundreds of thousands of events the Roman telescope will detect in order to find the 1% or fewer that are caused by exoplanetary systems. DeepMind might help us get better at deep space.

1. One problem astronomers encounter, however, is that the observed signal can be ambiguous. When a lone foreground star passes in front of a background star, the brightness of the background stars rises smoothly to a peak and then drops symmetrically to its original brightness. It's easy to understand mathematically and observationally.

2. But if the foreground star has a planet, the planet creates a separate brightness peak within the peak caused by the star. When trying to reconstruct the orbital configuration of the exoplanet that produced the signal, general relativity often allows two or more so-called degenerate solutions, all of which can explain the observations.

3. To date, astronomers have generally dealt with these degeneracies in simplistic and artificially distinct ways, Gaudi said. If the distant starlight passes close to the star, the observations could be interpreted either as a wide or a close orbit for the planet—an ambiguity astronomers can often resolve with other data. A second type of degeneracy occurs when the background starlight passes close to the planet. In this case, however, the two different solutions for the planetary orbit are generally only slightly different.

• According to Gaudi, these two simplifications of two-body gravitational microlensing are usually sufficient to determine the true masses and orbital distances.

• In fact, in a paper published last year, Zhang, Bloom, Gaudi and two other UC Berkeley co-authors, astronomy professor Jessica Lu and graduate student Casey Lam, described a new AI algorithm that does not rely on knowledge of these interpretations at all. The algorithm greatly accelerates analysis of microlensing observations, providing results in milliseconds, rather than days, and drastically reducing the computer crunching.

A.I. is Blooming New Advances in Astronomy

“A machine learning inference algorithm we previously developed led us to discover something new and fundamental about the equations that govern the general relativistic effect of light- bending by two massive bodies,” Joshua Bloom wrote in a blog post last year when he uploaded the paper to a preprint server, arXiv. Bloom is a UC Berkeley professor of astronomy and chair of the department.

New Unifying Theory

Kemin Zhang then tested the new AI algorithm on microlensing light curves from hundreds of possible orbital configurations of star and exoplanet and noticed something unusual: There were other ambiguities that the two interpretations did not account for. He concluded that the commonly used interpretations of microlensing were, in fact, just special cases of a broader theory that explains the full variety of ambiguities in microlensing events.

"The two previous theories of degeneracy deal with cases where the background star appears to pass close to the foreground star or the foreground planet," Zhang said. "The AI algorithm showed us hundreds of examples from not only these two cases, but also situations where the star doesn't pass close to either the star or planet and cannot be explained by either previous theory. That was key to us proposing the new unifying theory."

A new generation of Astronomers are working on improving A.I. to augment the future work to benefit all of humanity and civilization.

A Mathematical Treatment of the Offset Microlensing Degeneracy

Now Zhang and Gaudi have submitted (Submitted May 10th, 2022) a new paper that rigorously describes the new mathematics based on general relativity and explores the theory in microlensing situations where more than one exoplanet orbits a star.

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A.I. Could Evolve into a Useful Scientific Tool

"The AI suggested a way to look at the lens equation in a new light and uncover something really deep about the mathematics of it," said Bloom. "AI is sort of emerging as not just this kind of blunt tool that's in our toolbox, but as something that's actually quite clever. Alongside an expert like Keming, the two were able to do something pretty fundamental."

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