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Astrophysicist Daniel Grün uses artificial intelligence to explore the influence of dark matter and dark energy on the Universe.

Dirt on the lens is the last thing an astronomer needs – it ruins any observation. A lump of dirt in front of the lens, on the other hand, or a few obstacles that distract the light from its path, that’s exactly what Daniel Grün needs.

Grün, who launched the Chair of Astrophysics, Cosmology and Artificial Intelligence in July, wants to see things no human has seen before: he’s looking for traces of two phenomena physicists call dark matter and energy. black. One holds galaxies together, the other separates the Universe. Apart from this simplistic explanation, the properties of dark matter and dark energy have so far been very successful in escaping physical description. Daniel Grün therefore relies on an unusual set of tools in his research: an uneven cosmic glass, a large number of observed galaxies, tons of statistics and increasing amounts of artificial intelligence.

“Cosmology is in a tricky place,” says Grün. “We have a very successful model of our Universe that describes all of our observations well. But at the same time, we know that this model is wrong, that it is incomplete. Dark matter and dark energy are at the heart of the model, but nothing more is known about what they really are. The actual physical properties of the “dark universe” should leave traces – in the model and in reality.

The gravitational lens is the name of the approach that Grün wants to use to discover these traces. Weak gravitational lens, to be precise. The method is supposed to provide clues as to what is wrong with the successful but unexplained model of the Universe. The reasoning behind this is simple: Whenever light from distant galaxies must pass through huge masses on its way to Earth – other galaxies, galaxy clusters, black holes – their gravitational pull slightly deflects the light waves. In extreme cases, the image of a distant galaxy can even take the shape of a ring, as if astronomers are observing the galaxy through the bottom of a glass.

Looking through the cosmic glass
The effect Grün is betting on is more subtle. “The effect of a weak gravitational lens is like looking through a window that is not perfectly smooth,” explains the physicist. If the glass is not perfectly flat, if it has slight waves, the world behind it seems a little distorted. The same is true of looking into the Universe – the only difference being that the irregularity of the glass is replaced by the unevenly distributed and not directly visible matter that sits between the observer and distant galaxies and distorts the rays of light.

“In some ways, however, this effect is extremely frustrating,” says Daniel Grün. The problem is, the distorted image of a single galaxy doesn’t tell you anything – as little as the image of an apple in your neighbor’s backyard that you spy through uneven glass. It’s only when you compare it to an undistorted image of the apple that you can conclude what’s wrong with the glass. But in the cosmos, there is no such comparison to be made: there is only one Universe and only one Cosmic Glass.

Astronomers must therefore find other ways to help them achieve their goal, for example by studying many galaxies: if two of these structures – one close to Earth, the other far away – can be observed in the sky in the same line of sight and if the images of the two galaxies are slightly distorted, this could indicate an irregularity in the cosmic glass: an unknown mass between the galaxies and the Earth. But two distorted galaxies are not enough to draw reliable conclusions. More is needed. Much more.

To this end, more than 300 million galaxies have been observed by the Dark Energy Survey, a systematic sky survey for which Grün analyzed until recently the weak gravitational lensing effect. For six years, a camera located at the Inter-American Observatory at Cerro Tololo in Chile systematically scanned about one-eighth of the night sky. The result is not just an awe-inspiring map full of galaxies, the largest and deepest image of the sky to date. It is also the ideal basis for analyzing distorted images of all these galaxies using statistical methods – and therefore for drawing conclusions about invisible or dark matter.

Interior view of the dome of the 4-meter Victor M. Blanco telescope / Chile
Using a special camera on the telescope, astrophysicists like Daniel Gruen scan the sky.

© CTIO / NOIRLab / NSF / AURA / D. Munizaga
Comparisons with a baby photo
The result of this analysis is what Grün calls “the best picture to date of the distribution of matter in the adult Universe”. However, this image alone does not help in finding dark phenomena and errors in the cosmological model. Fortunately, there is also a baby image of the distribution of the material. It was taken around 380,000 years after the Big Bang, burned down in the cosmos, and reaches Earth today from all directions. Scientists therefore call it cosmic background radiation.

What Daniel Grün is asking now is: do these two images fit together? If we take the current models of cosmology, does the image of the baby inevitably become the image of the adult Universe? Or are there gaps, irregularities, which could give clues about the properties of dark effects?

There is no clear answer. “The analysis shows that the current structure is a little less pronounced than we expected,” says Grün. Just a little, though. Daniel Grün compares the dilemma to the scenario of a baby born with basketball genes who should be 7 feet tall as an adult. But in reality, they are only 6 feet 6 inches tall. Still a basketball player, but are the missing inches just a statistical fluctuation or an effect we don’t understand? “Unfortunately, there is no simple answer to this so far, no elegant solution that explains it all,” explains Daniel Grün.

Deep picture of the Universe
In search of mysterious phenomena such as dark energy or dark matter, astrophysicists like Daniel Grün roam the clusters of galaxies. AI is believed to help detect hidden structures in the Universe.

© ESO
And so the research continues. With new telescopes, but also with new ideas and new tools. The Dark Energy Spectroscopic Instrument (DESI), for example, with which Grün recently started working, can take photos of 5,000 galaxies at a time. Not only that: the camera, installed at the Kitt Peak Observatory in Arizona, also produces light fingerprints of individual galaxies. Physicists can use them to calculate exactly how far apart structures are. A three-dimensional map of the Universe behind its cosmic glass is thus created. And that could make it possible to obtain much more precise statistics on the distribution of the material.

The Legacy Survey of Space and Time, a survey of the sky conducted at the new Vera C. Rubin Observatory in Chile, and the European Euclid Space Telescope take a different approach. Their goal is to look even deeper into space and map billions of galaxies on their maps. They hope for more galaxies, more distorted images, better results.

Algorithms classify galaxies into groups
“It’s one side, it’s the data,” explains Daniel Grün. “But we also need to improve on the method side. When scientists look for tiny effects, discreet indications of an unusual distribution of matter, they shouldn’t make systematic mistakes. They also have to resort to new and previously unused statistical tips. And they need to understand all of this as best they can. “Even though a lot of work goes into creating new tools, we need to put at least as much energy into analyzing and understanding data,” says Grün. “It’s essential.”

Artificial intelligence, or AI for short, is expected to make an important contribution here, which is part of the reason why Grün’s chair was created as part of the Bavarian State AI Initiative. That said, the gravitational lens isn’t helped by the artificial intelligence that everyone is familiar with – say, the automatic image recognition you get on your cell phone: for this technology a lot of images are used to train algorithms to decide whether the image in an unknown photo is a dog or a cat.

The problem in cosmology is different. Here there are enormous amounts of data among which, on an individual level, almost nothing can be discerned – images of tiny galaxies which, taken on their own, tell us nothing. The task in cosmology is to decide whether these are characteristically distorted or not. “Basically what we have are hundreds of millions of tiny pictures where you can’t tell if it’s a dog or a cat. And what we want to know is if you can see a few more dogs or more cats, ”says Grün.

But the physicist, who has just returned from six years of work at Stanford in Calif., Wants to try to do even more with the computers of the University Observatory of Munich (USM): Analyzing hundreds of millions of galaxies presents a challenge. almost insoluble even for the best computer systems. In order to be able to reasonably calculate such volumes of data, the data must be cut into manageable chunks and described by models. This is where artificial intelligence comes in: it’s AI’s job to put each galaxy into one of many different groups based on how it looks, so those groups can then be analyzed.

Smart algorithms won’t just recognize and categorize galaxies. They will also develop their own models, such as models of the distribution of matter in the universe and the evolution of galaxies. Then, the results from the computer can be compared with real observation data.

We’ve just reached the point where AI is at the heart of this kind of cosmological analysis. We are at the point where nothing is possible without artificial intelligence.
Daniel Grün
Stay ahead of the data flow
This is a new kind of cosmology – the kind of paradigm shift that other disciplines like particle physics have seen for a long time. Gone are the days when astronomers would look through a telescope on their own and discover new objects. Now it’s all about big machines, international collaborations and data, data, data.

For scientists, this means that their tools and analysis must always be a little ahead of the exponentially growing stream of data. Not too far, otherwise it would be a waste of effort, but not too close either, otherwise the flood will overtake them and the data will be useless. Computers are proving more and more useful. “We have just reached the point where AI is at the heart of this kind of cosmological analysis,” says Daniel Grün. “We are at the point where nothing is possible without artificial intelligence.”

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