From Quanta Magazine (US): “Any Single Galaxy Reveals the Composition of an Entire Universe”

From Quanta Magazine (US)

January 20, 2022
Charlie Wood

Credit: Kaze Wong / CAMELS collaboration.

In the CAMELS project, coders simulated thousands of universes with diverse compositions, arrayed at the end of this video as cubes.

A group of scientists may have stumbled upon a radical new way to do cosmology.

Cosmologists usually determine the composition of the universe by observing as much of it as possible. But these researchers have found that a machine learning algorithm can scrutinize a single simulated galaxy and predict the overall makeup of the digital universe in which it exists — a feat analogous to analyzing a random grain of sand under a microscope and working out the mass of Eurasia. The machines appear to have found a pattern that might someday allow astronomers to draw sweeping conclusions about the real cosmos merely by studying its elemental building blocks.

“This is a completely different idea,” said Francisco Villaescusa-Navarro, a theoretical astrophysicist at The Flatiron Institute Center for Computational Astrophysics (US) and lead author of the work. “Instead of measuring these millions of galaxies, you can just take one. It’s really amazing that this works.”

It wasn’t supposed to. The improbable find grew out of an exercise Villaescusa-Navarro gave to Jupiter Ding, a Princeton University(US) undergraduate: Build a neural network that, knowing a galaxy’s properties, can estimate a couple of cosmological attributes. The assignment was meant merely to familiarize Ding with machine learning. Then they noticed that the computer was nailing the overall density of matter.

“I thought the student made a mistake,” Villaescusa-Navarro said. “It was a little bit hard for me to believe, to be honest.”

The results of the investigation that followed appeared on January 6 submitted for publication. The researchers analyzed 2,000 digital universes generated by The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project [The Astrophysical Journal]. These universes had a range of compositions, containing between 10% and 50% matter with the rest made up of Dark Energy, which drives the universe to expand faster and faster. (Our actual cosmos consists of roughly one-third Dark Matter and visible matter and two-thirds Dark Energy.) As the simulations ran, Dark Matter and visible matter swirled together into galaxies. The simulations also included rough treatments of complicated events like supernovas and jets that erupt from supermassive black holes.

Ding’s neural network studied nearly 1 million simulated galaxies within these diverse digital universes. From its godlike perspective, it knew each galaxy’s size, composition, mass, and more than a dozen other characteristics. It sought to relate this list of numbers to the density of matter in the parent universe.

It succeeded. When tested on thousands of fresh galaxies from dozens of universes it hadn’t previously examined, the neural network was able to predict the cosmic density of matter to within 10%. “It doesn’t matter which galaxy you are considering,” Villaescusa-Navarro said. “No one imagined this would be possible.”

“That one galaxy can get [the density to] 10% or so, that was very surprising to me,” said Volker Springel, an expert in simulating galaxy formation at The MPG Institute for Astrophysics [MPG Institut für Astrophysik](DE) who was not involved in the research.

The algorithm’s performance astonished researchers because galaxies are inherently chaotic objects. Some form all in one go, and others grow by eating their neighbors. Giant galaxies tend to hold onto their matter, while supernovas and black holes in dwarf galaxies might eject most of their visible matter. Still, every galaxy had somehow managed to keep close tabs on the overall density of matter in its universe.

One interpretation is “that the universe and/or galaxies are in some ways much simpler than we had imagined,” said Pauline Barmby, an astronomer at The Western University (CA). Another is that the simulations have unrecognized flaws.

The team spent half a year trying to understand how the neural network had gotten so wise. They checked to make sure the algorithm hadn’t just found some way to infer the density from the coding of the simulation rather than the galaxies themselves. “Neural networks are very powerful, but they are super lazy,” Villaescusa-Navarro said.

Through a series of experiments, the researchers got a sense of how the algorithm was divining the cosmic density. By repeatedly retraining the network while systematically obscuring different galactic properties, they zeroed in on the attributes that mattered most.

Near the top of the list was a property related to a galaxy’s rotation speed, which corresponds to how much matter (dark and otherwise) sits in the galaxy’s central zone. The finding matches physical intuition, according to Springel. In a universe overflowing with Dark Matter, you’d expect galaxies to grow heavier and spin faster. So you might guess that rotation speed would correlate with the cosmic matter density, although that relationship alone is too rough to have much predictive power.

The neural network found a much more precise and complicated relationship between 17 or so galactic properties and the matter density. This relationship persists despite galactic mergers, stellar explosions and black hole eruptions. “Once you get to more than [two properties], you can’t plot it and squint at it by eye and see the trend, but a neural network can,” said Shaun Hotchkiss, a cosmologist at The University of Auckland (NZ).

While the algorithm’s success raises the question of how many of the universe’s traits might be extracted from a thorough study of just one galaxy, cosmologists suspect that real-world applications will be limited. When Villaescusa-Navarro’s group tested their neural network on a different property — cosmic clumpiness — it found no pattern. And Springel expects that other cosmological attributes, such as the accelerating expansion of the universe due to Dark Energy, have little effect on individual galaxies.

The research does suggest that, in theory, an exhaustive study of the Milky Way and perhaps a few other nearby galaxies could enable an exquisitely precise measurement of our universe’s matter. Such an experiment, Villaescusa-Navarro said, could give clues to other numbers of cosmic import such as the sum of the unknown masses of the universe’s three types of neutrinos.

Neutrinos- Universe Today

But in practice, the technique would have to first overcome a major weakness. The CAMELS collaboration cooks up its universes using two different recipes. A neural network trained on one of the recipes makes bad density guesses when given galaxies that were baked according to the other. The cross-prediction failure indicates that the neural network is finding solutions unique to the rules of each recipe. It certainly wouldn’t know what to do with the Milky Way, a galaxy shaped by the real laws of physics. Before applying the technique to the real world, researchers will need to either make the simulations more realistic or adopt more general machine learning techniques — a tall order.

“I’m very impressed by the possibilities, but one needs to avoid being too carried away,” Springel said.

But Villaescusa-Navarro takes heart that the neural network was able to find patterns in the messy galaxies of two independent simulations. The digital discovery raises the odds that the real cosmos may be hiding a similar link between the large and the small.

“It’s a very beautiful thing,” he said. “It establishes a connection between the whole universe and a single galaxy.”

The Dark Energy Survey

Dark Energy Camera [DECam] built at DOE’s Fermi National Accelerator Laboratory(US).

NOIRLab National Optical Astronomy Observatory(US) Cerro Tololo Inter-American Observatory(CL) Victor M Blanco 4m Telescope which houses the Dark-Energy-Camera – DECam at Cerro Tololo, Chile at an altitude of 7200 feet.

NOIRLab(US)NSF NOIRLab NOAO (US) Cerro Tololo Inter-American Observatory(CL) approximately 80 km to the East of La Serena, Chile, at an altitude of 2200 meters.

Timeline of the Inflationary Universe WMAP.

The The Dark Energy Survey is an international, collaborative effort to map hundreds of millions of galaxies, detect thousands of supernovae, and find patterns of cosmic structure that will reveal the nature of the mysterious dark energy that is accelerating the expansion of our Universe. The Dark Energy Survey began searching the Southern skies on August 31, 2013.

According to Albert Einstein’s Theory of General Relativity, gravity should lead to a slowing of the cosmic expansion. Yet, in 1998, two teams of astronomers studying distant supernovae made the remarkable discovery that the expansion of the universe is speeding up.

Saul Perlmutter (center) [The Supernova Cosmology Project] shared the 2006 Shaw Prize in Astronomy, the 2011 Nobel Prize in Physics, and the 2015 Breakthrough Prize in Fundamental Physics with Brian P. Schmidt (right) and Adam Riess (left) [The High-z Supernova Search Team] for providing evidence that the expansion of the universe is accelerating.

To explain cosmic acceleration, cosmologists are faced with two possibilities: either 70% of the universe exists in an exotic form, now called Dark Energy, that exhibits a gravitational force opposite to the attractive gravity of ordinary matter, or General Relativity must be replaced by a new theory of gravity on cosmic scales.

The Dark Energy Survey is designed to probe the origin of the accelerating universe and help uncover the nature of Dark Energy by measuring the 14-billion-year history of cosmic expansion with high precision. More than 400 scientists from over 25 institutions in the United States, Spain, the United Kingdom, Brazil, Germany, Switzerland, and Australia are working on the project. The collaboration built and is using an extremely sensitive 570-Megapixel digital camera, DECam, mounted on the Blanco 4-meter telescope at Cerro Tololo Inter-American Observatory, high in the Chilean Andes, to carry out the project.

Over six years (2013-2019), the Dark Energy Survey collaboration used 758 nights of observation to carry out a deep, wide-area survey to record information from 300 million galaxies that are billions of light-years from Earth. The survey imaged 5000 square degrees of the southern sky in five optical filters to obtain detailed information about each galaxy. A fraction of the survey time is used to observe smaller patches of sky roughly once a week to discover and study thousands of supernovae and other astrophysical transients.

Fritz Zwicky discovered Dark Matter in the 1930s when observing the movement of the Coma Cluster., Vera Rubin a Woman in STEM, denied the Nobel, some 30 years later, did most of the work on Dark Matter.

Fritz Zwicky.
Coma cluster via NASA/ESA Hubble, the original example of Dark Matter discovered during observations by Fritz Zwicky and confirmed 30 years later by Vera Rubin.
In modern times, it was astronomer Fritz Zwicky, in the 1930s, who made the first observations of what we now call dark matter. His 1933 observations of the Coma Cluster of galaxies seemed to indicated it has a mass 500 times more than that previously calculated by Edwin Hubble. Furthermore, this extra mass seemed to be completely invisible. Although Zwicky’s observations were initially met with much skepticism, they were later confirmed by other groups of astronomers.

Thirty years later, astronomer Vera Rubin provided a huge piece of evidence for the existence of dark matter. She discovered that the centers of galaxies rotate at the same speed as their extremities, whereas, of course, they should rotate faster. Think of a vinyl LP on a record deck: its center rotates faster than its edge. That’s what logic dictates we should see in galaxies too. But we do not. The only way to explain this is if the whole galaxy is only the center of some much larger structure, as if it is only the label on the LP so to speak, causing the galaxy to have a consistent rotation speed from center to edge.

Vera Rubin, following Zwicky, postulated that the missing structure in galaxies is dark matter. Her ideas were met with much resistance from the astronomical community, but her observations have been confirmed and are seen today as pivotal proof of the existence of dark matter.
Astronomer Vera Rubin at the Lowell Observatory in 1965, worked on Dark Matter (The Carnegie Institution for Science).

Vera Rubin, with Department of Terrestrial Magnetism (DTM) image tube spectrograph attached to the Kitt Peak 84-inch telescope, 1970.

Vera Rubin measuring spectra, worked on Dark Matter(Emilio Segre Visual Archives AIP SPL).
Dark Matter Research

LBNL LZ Dark Matter Experiment (US) xenon detector at Sanford Underground Research Facility(US) Credit: Matt Kapust.

Lamda Cold Dark Matter Accerated Expansion of The universe http the-cosmic-inflation-suggests-the-existence-of-parallel-universes. Credit: Alex Mittelmann.

DAMA at Gran Sasso uses sodium iodide housed in copper to hunt for dark matter LNGS-INFN.

Yale HAYSTAC axion dark matter experiment at Yale’s Wright Lab.

DEAP Dark Matter detector, The DEAP-3600, suspended in the SNOLAB (CA) deep in Sudbury’s Creighton Mine.

The LBNL LZ Dark Matter Experiment (US) Dark Matter project at SURF, Lead, SD, USA.

DAMA-LIBRA Dark Matter experiment at the Italian National Institute for Nuclear Physics’ (INFN’s) Gran Sasso National Laboratories (LNGS) located in the Abruzzo region of central Italy.

DARWIN Dark Matter experiment. A design study for a next-generation, multi-ton dark matter detector in Europe at The University of Zurich [Universität Zürich](CH).

PandaX II Dark Matter experiment at Jin-ping Underground Laboratory (CJPL) in Sichuan, China.

Inside the Axion Dark Matter eXperiment U Washington (US) Credit : Mark Stone U. of Washington. Axion Dark Matter Experiment.

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Formerly known as Simons Science News, Quanta Magazine (US) is an editorially independent online publication launched by the Simons Foundation to enhance public understanding of science. Why Quanta? Albert Einstein called photons “quanta of light.” Our goal is to “illuminate science.” At Quanta Magazine, scientific accuracy is every bit as important as telling a good story. All of our articles are meticulously researched, reported, edited, copy-edited and fact-checked.