AI algorithms trained on real-world astronomical observations now outperform astronomers in
filtering through huge volumes of data to uncover new exploding stars, identify new
forms of galaxies, and detect massive star mergers, boosting the rate of new discovery
in the world's oldest science.
However, AI, also known as machine learning, can reveal something deeper, according to
astronomers at the University of California, Berkeley: previously unknown connections hidden
in the complex mathematics arising from general relativity – specifically, how that theory
is applied to finding new planets around other stars.
So, stick with us until the end as we investigate how Artificial Intelligence found previously
undetected space anomalies.
Things are about to get exciting!
Humans are not suitable for long-term space flight.
We are too fragile, too heavy, and demand a great deal of resources to survive.
When it comes to exploring our solar system and the worlds beyond, sending machines makes
more sense.
Unfortunately, machines lack our natural curiosity.
A spaceship built to capture the surface of a planet would not be startled if an alien
raced across the screen and switch its focus, it would simply continue taking pictures of
rocks.
Steve Chien and his colleagues are working to change that.
Chien leads the department at NASA's Jet Propulsion Laboratory (JPL) that oversees artificial
intelligence throughout mission preparation.
JPL is the birthplace of missions like Voyager, which travelled to the outer reaches of the
solar system, and New Horizons, which buzzed by Pluto.
In his day work, he develops software and algorithms that will allow deep space probes
and planetary rovers, such as the Rosetta mission to a comet and Perseverance to Mars,
to better mimic human thought and behavior.
He intends to transform spacecraft into actual explorers, not just collections of cameras
and sensors propelled by a rocket motor.
A study that was published in the journal Nature Astronomy, the researchers explain
how an AI algorithm designed 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 long-standing theories currently used to explain these observations
are woefully inadequate.
In 1936, Albert Einstein used his new theory of general relativity to demonstrate how the
gravity of a foreground star can bend light from a distant star, not only brightening
it as seen from Earth, but also splitting it into several points of light or distorting
it into a ring, now known as an Einstein ring.
This is comparable to how a hand lens may concentrate and enhance sunlight.
When the foreground object is a star with a planet, however, the brightening over time
– the light curve – is more complicated.
Furthermore, there are frequently numerous planetary orbits that can describe a given
light curve as well – these are known as degeneracies.
That is when humans erroneously simplified the math and missed the big picture.
The AI algorithm, on the other hand, pointed to a mathematical way to unify the two major
types of degeneracy in interpreting what telescopes detect during microlensing, demonstrating
that the two "theories" are actually subsets of a larger theory that, the scientists admit,
is likely still incomplete.
"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 posted on his blog a year ago when he
submitted the paper to the arXiv preprint server.
Bloom is a UC Berkeley astronomy professor and department chair.
He linked UC Berkeley graduate student Keming Zhang's discovery to connections made lately
by Google's AI team, DeepMind, between two different fields of mathematics.
These examples indicate that AI systems can uncover fundamental relationships that humans
miss.
"I argue that they constitute one of the first, if not the first time 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."
"This is a milestone in AI and machine learning," said co-author Scott Gaudi, an astronomy professor
at The Ohio State University and one of the pioneers of exploiting 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."
More than 5,000 exoplanets, or extrasolar planets, have been detected orbiting stars
in the Milky Way, yet only a few have been observed with a telescope because they are
too faint.
Most have been identified because they generate a Doppler wobble in their host stars' motions
or because they somewhat shade the light from the host star when they pass in front of it
– transits that were the focus of NASA's Kepler mission.
A third technique, microlensing, has uncovered a little more than 100.
The discovery of thousands more exoplanets by microlensing is one of the primary goals
of NASA's Nancy Grace Roman Space Telescope, which is scheduled to launch in 2027.
The approach has an edge over Doppler and transit in that it can identify lower-mass
planets, such as those the size of Earth, that are located far from their stars, at
a distance equal to Jupiter or Saturn in our solar system.
Bloom, Zhang, and their colleagues set out two years ago to create an artificial intelligence
algorithm to examine microlensing data faster in order to calculate the stellar and planetary
masses of these systems, as well as the distances the planets orbit their stars.
An algorithm like this would speed up examination of the hundreds of thousands of events that
the Roman telescope is anticipated to detect in order to locate the 1% or less that are
triggered by exoplanetary systems.
Zhang then used the new AI technique to microlensing light curves from hundreds of different star
and exoplanet orbital configurations and discovered something unusual: there were further discrepancies
that the two interpretations did not account for.
He came to the conclusion that the generally used interpretations of microlensing were
simply particular cases of a larger theory that explains the complete range of ambiguity
in microlensing events.
"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."
Fast Forward to August of this year, researchers yet again using artificial intelligence discovered
something surprising.
The SNAD team, a global network of researchers, uncovered 11 previously unknown space anomalies,
seven of which are supernova candidates.
The findings were reported in the journal 'New Astronomy.'
The researchers used a k-D tree to discover anomalies in digital images of the Northern
sky collected in 2018 using the 'nearest neighbor' method.
Machine learning algorithms aided in the automation of the search.
The majority of astronomical discoveries have been based on observations followed by computations.
While the total number of observations remained relatively low in the twentieth century, the
volume of data significantly grew with the entrance of large-scale astronomical surveys.
For example, the Zwicky Transient Facility (ZTF), which surveys the Northern sky with
a wide-field view camera, generates 1.4 Terabytes of data per night of observation and has a
library containing billions of items.
Manually analyzing such massive volumes of data is both costly and time-consuming, so
the SNAD team of researchers from Russia, France, and the United States collaborated
to create an automated solution.
When scientists investigate astronomical objects, they look at their light curves, which illustrate
how an object's brightness changes over time.
The observers first recognize a flash of light in the sky and then track its evolution to
see if the light gets brighter, weaker, or goes out over time.
The researchers evaluated a million real light curves from the ZTF's 2018 catalogue as well
as seven simulated live curve models of the types of items under consideration in this
study.
They tracked 40 variables in total, including the amplitude of an object's brightness and
the timeframe.
‘We described the properties of our simulations using a set of characteristics expected to
be observed in real astronomical bodies.
In the dataset of approximately a million objects, we were looking for super-powerful
supernovae, Type one-A supernovae, Type 2 supernovae, and tidal disruption events,’
explains Konstantin Malanchev.
‘We refer to such classes of objects as anomalies.
They are either very rare, with little-known properties, or appear interesting enough to
merit further study.’
Using the k-D tree algorithm, the light curve data from real objects was compared to that
of simulations.
A k-D tree is a geometric data structure that divides space into smaller portions by using
hyperplanes, planes, lines, or points to cut it.
This approach was utilized in the current study to narrow down the search range when
seeking for real objects with properties similar to those specified in the seven simulations.
Following that, the team found 15 nearest neighbors, that is, real objects from the
ZTF database, for each simulation—a total of 105 matches—which the researchers then
visually checked for anomalies.
The manual verification validated 11 anomalies, seven of which were supernova candidates and
four of which were active galactic nuclei candidates for tidal disruption events.
‘This is a very good result, in addition to the already-discovered rare objects, we
were able to detect several new ones previously missed by astronomers.
This means that existing search algorithms can be improved to avoid missing such objects.’
‘Astronomical and astrophysical phenomena which have not yet been discovered are in
fact anomalies,’ according to Matvey Kornilov, Associate Professor of the HSE University
Faculty of Physics.
‘Their observed manifestations are expected to differ from the properties of known objects.
In the future, we will try using our method to discover new classes of objects.’
This study reveals that the technique is highly effective while remaining relatively simple
to implement.
The proposed algorithm for detecting specific forms of space phenomena is universal and
may be used to find any fascinating astronomical objects, not just unusual varieties of supernovae.
Will AI uncover new objects in space, or perhaps alien life?
Can artificial intelligence truly assist scientists and astronomers in unravelling the secrets
of the universe?
Tell us in the comments.