The heart of the Milky Way can be a mysterious place. A gigantic black hole resides there, and it’s surrounded by a retinue of stars that astronomers call a Nuclear Star Cluster (NSC). The NSC is one of the densest populations of stars in the Universe. There are about 20 million stars in the innermost 26 light years of the galaxy.
New research shows that about 7% of the stars in the NSC came from a single source: a globular cluster of stars that fell into the Milky Way between 3 and 5 billion years ago.
In the 1920s, Edwin Hubble studied hundreds of galaxies. He found that they tended to fall into a few broad types. Some contained elegant spirals of bright stars, while others were spherical or elliptical with little or no internal structure. In 1926 he developed a classification scheme for galaxies, now known as Hubble’s Tuning Fork.
Hubble’s tuning fork diagram for galaxies. Credit: Edwin Hubble
When you look at Hubble’s scheme, it suggests an evolution of galaxies, beginning as an elliptical galaxy, then flattening and shifting into a spiral galaxy. While many saw this as a reasonable model, Hubble cautioned against jumping to conclusions. We now know ellipticals do not evolve into spirals, and the evolution of galaxies is complex. But Hubble’s scheme marks the beginning of the attempt to understand how galaxies grow, live, and die.
According to the most widely accepted cosmological theories, the first stars in the Universe formed a few hundred million years after the Big Bang. Unfortunately, astronomers have been unable to “see” them since their emergence coincided during the cosmological period known as the “Dark Ages.” During this period, which ended about 13 billion years ago, clouds of gas filled the Universe that obscured visible and infrared light.
However, astronomers have learned that light from this era can be detected as faint radio signals. It’s for this reason that radio telescopes like the Murchison Widefield Array (MWA) were built. Using data obtained by this array last year, an international team of researchers is scouring the most precise radio data to date from the early Universe in an attempt to see exactly when the cosmic “Dark Ages” ended.
For some time, astronomers have known that collisions or mergers between galaxies are an integral part of cosmic evolution. In addition to causing galaxies to grow, these mergers also trigger new rounds of star formation as fresh gas and dust are injected into the galaxy. In the future, astronomers estimate that the Milky Way Galaxy will merge with the Andromeda Galaxy, as well as the Small and Large Magellanic Clouds in the meantime.
According to new results obtained by researchers at the Flatiron Institute’s Center for Computational Astrophysics (CCA) in New York city, the results of our eventual merger with the Magellanic Clouds is already being felt. According to results presented at the 235th meeting of the American Astronomical Society this week, stars forming in the outskirts of our galaxy could be the result of these dwarf galaxies merging with our own.
Since the mid-20th century, scientists have had a pretty good idea of how the Universe came to be. Cosmic expansion and the discovery of the Cosmic Microwave Background (CMB) lent credibility to the Big Bang Theory, and the accelerating rate of expansion led to theories about Dark Energy. Still, there is much about the early Universe that scientists still don’t know, which requires that they rely on simulations on cosmic evolution.
This has traditionally posed a bit of a problem since the limitations of computing meant that simulation could either be large scale or detailed, but not both. However, a team of scientists from Germany and the United States recently completed the most detailed large-scale simulation to date. Known as TNG50, this state-of-the-art simulation will allow researchers to study how the cosmos evolved in both detail and a large scale.
It’s a difficult thing to wrap your head around sometimes. Though it might feel stationary, planet Earth is actually moving at an average velocity of 29.78 km/s (107,200 km/h; 66600 mph). And yet, our planet has nothing on the Sun itself, which travels around the center of our galaxy at a velocity of 220 km/s (792,000 km/h; 492,000 mph).
But as is so often the case with our Universe, things only get more staggering the farther you look. According to a new study by an international team of astronomers, the most massive “super spiral” galaxies in the Universe rotate twice as fast as the Milky Way. The cause, they argue, is the massive clouds (or halos) of Dark Matter that surround these galaxies.
This week we welcome Dr. Marina Kounkel, a postdoctoral scholar in the Physics and Astronomy Department at the Western Washington University. Her research focuses on observing the dynamics of young stars.
Since the birth of modern astronomy, scientists have sought to determine the full extent of the Milky Way galaxy and learn more about its structure, formation and evolution. According to current theories, it is widely believed that the Milky Way formed shortly after the Big Bang (roughly 13.51 billion years ago). This was the result of the first stars and star clusters coming together, as well as the accretion of gas directly from the Galactic halo.
A lot of attention has been dedicated to the machine learning technique known as “deep learning”, where computers are capable of discerning patterns in data without being specifically programmed to do so. In recent years, this technique has been applied to a number of applications, which include voice and facial recognition for social media platforms like Facebook.
However, astronomers are also benefiting from deep learning, which is helping them to analyze images of galaxies and understand how they form and evolve. In a new study, a team of international researchers used a deep learning algorithm to analyze images of galaxies from the Hubble Space Telescope. This method proved effective at classifying these galaxies based on what stage they were in their evolution.
A ‘deep learning’ algorithm trained on images from cosmological simulations is surprisingly successful at classifying real galaxies in Hubble images. Credit: HST/CANDELS
In the past, Marc Huertas-Company has already applied deep learning methods to Hubble data for the sake of galaxy classification. In collaboration with David Koo and Joel Primack, both of whom are professor emeritus’ at UC Santa Cruz (and with support from Google), Huertas-Company and the team spent the past two summers developing a neural network that could identify galaxies at different stages in their evolution.
“This project was just one of several ideas we had,” said Koo in a recent USCS press release. “We wanted to pick a process that theorists can define clearly based on the simulations, and that has something to do with how a galaxy looks, then have the deep learning algorithm look for it in the observations. We’re just beginning to explore this new way of doing research. It’s a new way of melding theory and observations.”
For the sake of their study, the researchers used computer simulations to generate mock images of galaxies as they would look in observations by the Hubble Space Telescope. The mock images were used to train the deep learning neural network to recognize three key phases of galaxy evolution that had been previously identified in the simulations. The researchers then used the network to analyze a large set of actual Hubble images.
As with previous images anaylzed by Huertas-Company, these images part of Hubble’s Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) project – the largest project in the history of the Hubble Space Telescope. What they found was that the neural network’s classifications of simulated and real galaxies was remarkably consistent. As Joel Primack explained:
“We were not expecting it to be all that successful. I’m amazed at how powerful this is. We know the simulations have limitations, so we don’t want to make too strong a claim. But we don’t think this is just a lucky fluke.”
A spiral galaxy ablaze in the blue light of young stars from ongoing star formation (left) and an elliptical galaxy bathed in the red light of old stars (right). Credit: SDSS
The research team was especially interested in galaxies that have a small, dense, star-forming region known as a “blue nugget”. These regions occur early in the evolution of gas-rich galaxies, when big flows of gas into the center of a galaxy cause the formation of young stars that emit blue light. To simulate these and other types of galaxies, the team relied on state-of-the-art VELA simulations developed by Primack and an international team of collaborators.
In both the simulated and observational data, the computer program found that the “blue nugget” phase occurs only in galaxies with masses within a certain range. This was followed by star formation ending in the central region, leading to the compact “red nugget” phase, where the stars in the central region exit their main sequence phase and become red giants.
The consistency of the mass range was exciting because it indicated that the neural network was identifying a pattern that results from a key physical process in real galaxies – and without having to be specifically told to do so. As Koo indicated, this study as a big step forward for astronomy and AI, but a lot of research still needs to be done:
“The VELA simulations have had a lot of success in terms of helping us understand the CANDELS observations. Nobody has perfect simulations, though. As we continue this work, we will keep developing better simulations.”
Artist’s representation of an active galactic nucleus (AGN) at the center of a galaxy. Credit: NASA/CXC/M.Weiss
For instance, the team’s simulations did not include the role played by Active Galactic Nuclei (AGN). In larger galaxies, gas and dust is accreted onto a central Supermassive Black Hole (SMBH) at the core, which causes gas and radiation to be ejected in huge jets. Some recent studies have indicated how this may have an arresting effect on star formation in galaxies.
However, observations of distant, younger galaxies have shown evidence of the phenomenon observed in the team’s simulations, where gas-rich cores lead to the blue nugget phase. According to Koo, using deep learning to study galactic evolution has the potential to reveal previously undetected aspects of observational data. Instead of observing galaxies as snapshots in time, astronomers will be able to simulate how they evolve over billions of years.
“Deep learning looks for patterns, and the machine can see patterns that are so complex that we humans don’t see them,” he said. “We want to do a lot more testing of this approach, but in this proof-of-concept study, the machine seemed to successfully find in the data the different stages of galaxy evolution identified in the simulations.”
In the future, astronomers will have more observation data to analyze thanks to the deployment of next-generation telescopes like the Large Synoptic Survey Telescope (LSST), the James Webb Space Telescope (JWST), and the Wide-Field Infrared Survey Telescope (WFIRST). These telescopes will provide even more massive datasets, which can then be analyzed by machine learning methods to determine what patterns exist.
Astronomy and artificial intelligence, working together to better our understanding of the Universe. I wonder if we should put it on the task of finding a Theory of Everything (ToE) too!
Since the 1960s, astrophysicists have postulated that in addition to all the matter that we can see, the Universe is also filled with a mysterious, invisible mass. Known as “Dark Matter”, it’s existence was proposed to explain the “missing mass” of the Universe, and is now considered a fundamental part of it. Not only is it theorized to make up about 80% of the Universe’s mass, it is also believed to have played a vital role in the formation and evolution of galaxies.
However, a recent finding may throw this entire cosmological perspective sideways. Based on observations made using the NASA/ESA Hubble Space Telescope and other observatories around the world, astronomers have found a nearby galaxy (NGC 1052-DF2) that does not appear to have any dark matter. This object is unique among galaxies studied so far, and could force a reevaluation of our predominant cosmological models.
Image of the ultra diffuse galaxy NGC 1052-DF2, created from images forming part of the Digitized Sky Survey 2. Credit:ESA/Hubble, NASA, Digitized Sky Survey 2. Acknowledgement: Davide de Martin
For the sake of their study, the team consulted data from the Dragonfly Telephoto Array (DFA), which was used to identify NGC 1052-DF2. Based on data from Hubble, the team was able to determined its distance – 65 million light-years from the Solar System – as well as its size and brightness. In addition, the team discovered that NGC 1052-DF52 is larger than the Milky Way but contains about 250 times fewer stars, which makes it an ultra diffuse galaxy.
As van Dokkum explained, NGC 1052-DF2 is so diffuse that it’s essentially transparent. “I spent an hour just staring at this image,” he said. “This thing is astonishing: a gigantic blob so sparse that you see the galaxies behind it. It is literally a see-through galaxy.”
Using data from the Sloan Digital Sky Survey (SDSS), the Gemini Observatory, and the Keck Observatory, the team studied the galaxy in more detail. By measuring the dynamical properties of ten globular clusters orbiting the galaxy, the team was able to infer an independent value of the galaxy’s mass – which is comparable to the mass of the stars in the galaxy.
This led the team to conclude that either NGC 1052-DF2 contains at least 400 times less dark matter than is predicted for a galaxy of its mass, or none at all. Such a finding is unprecedented in the history of modern astronomy and defied all predictions. As Allison Merritt – an astronomer from Yale University, the Max Planck Institute for Astronomy and a co-author on the paper – explained:
“Dark matter is conventionally believed to be an integral part of all galaxies — the glue that holds them together and the underlying scaffolding upon which they are built…There is no theory that predicts these types of galaxies — how you actually go about forming one of these things is completely unknown.”
“This invisible, mysterious substance is by far the most dominant aspect of any galaxy. Finding a galaxy without any is completely unexpected; it challenges standard ideas of how galaxies work,” added van Dokkum.
However, it is important to note that the discovery of a galaxy without dark matter does not disprove the theory that dark matter exists. In truth, it merely demonstrates that dark matter and galaxies are capable of being separate, which could mean that dark matter is bound to ordinary matter through no force other than gravity. As such, it could actually help scientists refine their theories of dark matter and its role in galaxy formation and evolution.
In the meantime, the researchers already have some ideas as to why dark matter is missing from NGC 1052-DF2. On the one hand, it could have been the result of a cataclysmic event, where the birth of a multitude of massive stars swept out all the gas and dark matter. On the other hand, the growth of the nearby massive elliptical galaxy (NGC 1052) billions of years ago could have played a role in this deficiency.
However, these theories do not explain how the galaxy formed. To address this, the team is analyzing images that Hubble took of 23 other ultra-diffuse galaxies for more dark-matter deficient galaxies. Already, they have found three that appear to be similar to NGC 1052-DF2, which could indicate that dark-matter deficient galaxies could be a relatively common occurrence.
If these latest findings demonstrate anything, it is that the Universe is like an onion. Just when you think you have it figured out, you peal back an additional layer and find a whole new set of mysteries. They also demonstrate that after 28 years of faithful service, the Hubble Space Telescope is still capable of teaching us new things. Good thing too, seeing as the launch of its successor has been delayed until 2020!
