﻿Unless you’ve been living under a rock for the last three years, you’ll have seen that AI is
changing society in a big way. It’s shown up in workplaces,
or unexpectedly on your phone. For some, ChatGPT is replacing Google as the new way to search,
and governments and companies around the globe are getting in on the action.
So, it was really only a matter of time before someone tried to use AI to do science.
In fact, this is no new phenomenon. Scientists have been using AI in some capacity or another for
decades, although perhaps not by that name. But since the meteoric rise of Large Language
Models, AI in science is becoming increasingly widespread, in all
fields - including physics and astronomy. That may worry you, or it may excite you. There
is much that AI companies claim AI can do; can AI possibly make a good scientist as well?
The answer may surprise you. With the right guidance - yes. AI and machine learning tools
aren’t just useful; scientists are using them to help answer questions that no other method can.
AI has the potential to revolutionise our understanding of the universe forever.
Provided we don’t use it too much. I’m Alex McColgan, and you’re watching
Astrum. Join me today as we explore some of the best uses of AI in astronomy and physics,
and see the areas of science where AI might not just be helpful, but could be the only
tool that can possibly get the job done - as long as we can avoid its pitfalls.
Let me start with a story. In September 2023, Bruno Altieri, an archive scientist working on
data from the brand new Euclid telescope, saw something fuzzy in one of the images.
Launched in July 2023, Euclid had been busily surveying a huge swathe of the sky - over a
third of it - in an effort to track dark matter and dark energy in galaxies.
And what Altieri saw around Galaxy NGC 6505 was a perfect, naturally occurring phenomenon for doing
just that. Altieri had found an Einstein ring, and when higher resolution images came through
scientists were able to see it in all its glory. Einstein rings are an incredibly rare mirage of
the universe - the ring you’re seeing in this image is not a truly physical object,
but is actually an example of strong gravitational lensing, where the light from a second galaxy
hiding behind the first has its light bent towards us, thanks to the first galaxy’s gravity.
Due to the way the gravity from the first galaxy curves space, if the two galaxies are lined up
just right, this curvature of light happens equally around all sides of the first galaxy,
redirecting perfectly to create the impression of a complete ring. The conditions for this occurring
are incredibly rare - if the two galaxies are even a little off, then you will only get an arc rather
than a full ring, or will get nothing at all. But though rare, Einstein Rings are extremely
useful. Scientists can use them to calculate the mass of everything within the ring - in this case,
NGC 6505’s galactic nucleus - providing them a second tool to detect mass. And
if there’s more mass detected within the ring than we can physically see,
then the difference is likely dark matter. Einstein Rings can also let you see further
galaxies than your telescope could normally detect, which can be analysed to learn about
the more distant galaxy - there are a lot of benefits. But here’s the thing - this discovery,
while fortunate, was not how most Einstein rings will be discovered going forward.
The size of the images Euclid is producing is truly daunting. To be clear, the maps being
generated are sized in Petabytes - hundreds of them. To put that into scale, your computer at
home is likely running either gigabytes or maybe up to a handful of terabytes of storage space,
if it’s particularly high end - each terabyte being 1000 gigabytes. But a petabyte is 1000
terabytes. To store some of these star maps properly, you need the storage equivalent of
nearly a thousand computers. The data contained in Euclid’s maps will be huge. Just 1% of the
completed map was released in late 2024, and that segment already contains 100 million sources of
light - either stars or entire galaxies. That’s just too many for the human mind to handle.
There will be thousands of Einstein rings hiding amongst all that data,
all those stars. But to find them on our own would take far too long, unless we wanted
to take generations in the attempt. It gets worse. Strong gravitational lensing
isn’t even the main way that Euclid wants to detect dark matter. Instead,
it will be looking for examples of weak gravitational lensing. The idea is similar,
but instead of a middle galaxy redirecting light, it’s an entire galaxy cluster. And instead of a
single galaxy behind them having its light bent, it’s a number of galaxies that are deviating from
the statistical average. You might be asking how on Earth that possibly works. Let me explain.
There are enough stars and galaxies in the universe that we can predict on average how close
together they are likely to be. Which means, if you look at any particular bunch of stars, you can
see if they deviate from that statistical average or not. To detect weak gravitational lensing,
you are looking to see if the light sources behind a clump of galaxies are deviating in
such a way that the statistical deviation starts to look like a ring. Galaxies appearing where
they probably aren’t really, because their light is being weakly bent by intervening mass.
Good luck spotting that just with your eyeballs.
In other respects, weak gravitational lensing works the same as strong gravitational
lensing - you can model objects with the resulting ring, to detect the presence of invisible dark
matter. This high-level calculation is the way Euclid hopes to map much of the dark matter in
the third of the night sky it’s looking at. Finding examples like this is something you’d
never hope to be able to do by the old-fashioned astronomy technique of
pointing your home telescope at the sky. But this is where AI shines.
AI - specifically “machine learning” in this case - can be trained to identify examples of weak or
strong gravitational lensing in all that data. We have examples where it’s already done this,
such as when astronomers used AI to identify 56 gravitational lens candidates from images
taken by the VLT in Chile. We have further examples where it’s
been put to good use doing similar tasks. In a citizen science initiative in Japan,
10,000 human volunteers sifted through data from the Subaru telescope, identifying 20,000 galaxies.
From this, an AI was trained to identify the pattern, and discovered a further 410,000
galaxies in the data, with 98% accuracy. Machine learning is an incredible tool in
astronomy, and has helped find exoplanets in Kepler data. So it can certainly be taught
to repeatedly spot Einstein rings or weak gravitational lensing in the Euclid data.
Then it’s just about having a powerful enough computer (or network of computers) to run
the AI and feed it the petabytes of data, and seeing what patterns it detects.
This new tool will let us look at the universe in ways we never would
have been able to do without it. Even if the accuracy isn’t 100%, our understanding will
undeniably improve. Who knows what mysteries large patterns like this could solve?
But AI is not just being used by scientists to notice patterns.
Large Language Models are beginning to see use in inventing experiments.
LIGO is a pair of 4km long gravitational-wave observatories that each use two gigantic
laser interferometers to detect waves in gravity caused by massive events in the
universe. By spotting these waves, LIGO has witnessed many black holes merging,
with new ones being uncovered every few days. But while its sensitivity is good enough to notice a
change in the distance between its mirrors of 1/10,000th of the width of a proton (and you
can see my video about it to learn more about that), there is always room for improvement.
Rana Adhikari, one of the designers who helped optimise LIGO in the mid-2000’s,
wanted to see if more could be done to increase LIGO’s sensitivity. He turned to a specially
developed AI called “Urania”, and after feeding it information about the sorts of components and
devices it was allowed to use, set it to work designing whatever it wanted to make.
And initially, what Urania made was ridiculous. Incomprehensible messes,
without symmetry or sense. Still, the team kept refining their inputs, and in time the
AI got better at what it produced. It still was creating designs that looked ridiculous,
or alien. But strangely, the designs now worked. If they’d been built into LIGO, they would have
improved sensitivity by 10-15%. The only issue was, Adhikari and his team had no idea why.
This was one of the quirks of AI experiments. LLMs and other AI are so vast, so complex, that
it is not always easy or possible to follow their reasoning. Eventually, after months of evaluation,
Adhikari’s team worked out what the AI had been doing. It had made use of a theoretical
principle come up with by Russian physicists decades ago to reduce quantum mechanical noise,
an idea that hadn’t been used yet in mainstream experiments. The AI had incorporated the idea
into its design, and had created a huge 3km ring between the detector and the main
interferometer to circulate the light. While it’s a little late for LIGO,
future generations of gravitational wave detectors can take learnings like this one
and incorporate them into their designs. So, with the proper guidance, and the right
patterns to follow, AI can find patterns in data and can even invent new experiments,
using outside the box thinking that’s not blinded by tradition or accepted practice. So it's no
wonder that scientists are increasingly turning to it, if only to see how it might innovate.
In 2023, the journal Nature found that in the last decade the number of research papers that
mentioned AI in the title or abstract had risen to 8%, a big rise from the 2% a decade earlier.
Personally, I would not be surprised if that number continued to rise,
as over half of 1,600 scientists polled said that AI sped up computations, processed data faster,
and saved time and money. AI can even get involved in the writing process,
improving the readability of papers (which can be a little dense), or translating them into
other languages - increasing the number of people who will read a scientist’s paper. Furthermore,
around 25% of scientists polled even said one of the benefits of AI was using it to
brainstorm new ideas, and 15% believed it could generate entire new research hypotheses.
Machine learning can be incredibly useful, and it’s exciting to see what AI will come
up with if asked to design experiments and hypotheses. And so far I’ve been
pretty positive about it in this video. But there is another side to this coin. Why
are scientists also concerned about increasing AI use in science?
Let’s start with a minor point, and then develop it. As my earlier example highlighted,
we do not always understand why AI does the things it does. While we can acknowledge that
certain AI experiments do work, results without understanding those results are not actually that
useful. So in that way, AI can’t be left to do everything. After all, the goal of science is
to gain understanding of the universe. Just being delivered the results without knowing how we got
there becomes as arbitrary and meaningless as being told the universe’s answer is 42.
But it goes further than that - the results being delivered need to be factual. If we do not
understand how an AI arrived at an answer, there is no guarantee that the answer is factual.
In a study released in 2024, the Royal Society - the UK’s national academy of science - called
for data curators and information managers to ensure that data didn’t get contaminated or
outright fabricated by AI - because that is a thing that can happen, if an AI thinks it
is being helpful by doing so. They also warned a growing number of studies using Machine Learning
were impossible to reproduce, which meant that no one could double check the study - particularly
troubling when the scientists who ran them had no deep understanding of the processes
the AI was using to get its results. This isn’t an isolated paper. In that nature
poll of 1,600 scientists, nearly 70% of them worried that AI could proliferate misinformation,
nearly 70% thought AI made plagiarism harder to detect, and more than 60% worried it could
bring mistakes and outright deceptions into research. Nearly 50% claimed AI results could
entrench bias or discrimination in data - after all, AI mimics us, and humans are hardly without
bias. Bad data in, bad data out. Thankfully in astronomy it’s easy enough
to double-check an AI’s workings - if they say a galaxy is there,
all you have to do is point a telescope at the location to see if they’re right.
But by the time we start talking about tens of thousands to millions of objects
being detected, it becomes increasingly difficult to authenticate everything.
While this is arguably a reasonable trade-off - a little dip in accuracy
to get some broad-brushstrokes of patterns from a colossal data pool - this must be kept in mind
when using AI. For tasks that only an AI can achieve, you really need to be able to trust
your AI - or at least know its limitations, and you should always double check its work.
AI right now is certainly a useful tool to help scientists know where to look to find answers,
and may offer some outside-the-box thinking that we had never dreamed of. There is a future with
AI, where discoveries are made with benefits that are real, measurable and tangible.
I’m very excited for what machine learning will uncover in the Euclid data in October 2026.
But the key to science is being able to know the difference between what’s real,
and what’s not. Until AI can do that, let’s leave telling the difference to the humans.