Nyx, a vast new stellar stream discovered in the vicinity of the Sun, named after the Greek goddess of the night, may provide the first indication that a dwarf galaxy had merged with the Milky Way disk. These stellar streams are thought to be globular clusters or dwarf galaxies that have been stretched out along its orbit by tidal forces before being completely disrupted.
Among the oldest objects in the universe, the Milky Way is surrounded by about 150 globular clusters, formed about 11.5 billion years ago, 2.3 billion years after the Big Bang and shortly before the rate of cosmic star formation reached its peak, 10 billion years ago –a period known as “cosmic high noon.”
“If there are any clumps of stars that are moving together in a particular fashion, that usually tells us that there is a reason that they’re moving together,” says Lina Necib, who discovered the star stream. Necib a postdoctoral scholar in theoretical physics at Caltech, who studies the kinematics—or motions—of stars and dark matter in the Milky Way.
Starting at the Beginning of Time
Since 2014, researchers from Caltech, Northwestern University, UC San Diego and UC Berkeley, among other institutions, have been developing highly-detailed simulations of realistic galaxies as part of a project called FIRE (Feedback In Realistic Environments) that seeks to develop and explore cosmological simulations of galaxy formation. The simulations directly resolve the interstellar medium of individual galaxies while capturing their cosmological environment.starting from the virtual equivalent of the beginning of time, the simulations produce galaxies that look and act much like our own.
The researchers also incorporate data from Gaia space observatory was launched in December of 2013 by the European Space Agency. to create an extraordinarily precise three-dimensional map of about one billion stars throughout the Milky Way galaxy and beyond. Gaia’s stunning first dataset, published in 2016, cataloged more than a billion stars and contained distance and motion data for 2 million stars.
“It’s the largest kinematic study to date. The observatory provides the motions of one billion stars,” Necib,explained. “A subset of it, seven million stars, have 3-D velocities, which means that we can know exactly where a star is and its motion. We’ve gone from very small datasets to doing massive analyses that we couldn’t do before to understand the structure of the Milky Way.”
“Galaxies form by swallowing other galaxies,” Necib said, addressing the question of how the Milky Way became what we see today.. “We’ve assumed that the Milky Way had a quiet merger history, and for a while it was concerning how quiet it was because our simulations show a lot of mergers. Now, with access to a lot of smaller structures, we understand it wasn’t as quiet as it seemed. It’s very powerful to have all these tools, data and simulations. All of them have to be used at once to disentangle this problem. We’re at the beginning stages of being able to really understand the formation of the Milky way.”
Neural Networks– Huge Datasets of the “Fire” Galaxies
“Before, astronomers had to do a lot of looking and plotting, and maybe use some clustering algorithms. But that’s not really possible anymore,” Necib said. “We can’t stare at seven million stars and figure out what they’re doing. What we did in this series of projects was use the Gaia mock catalogs.”
The Gaia mock catalog, developed by Robyn Sanderson (University of Pennsylvania), essentially asked: ‘If the FIRE simulations were real and observed with Gaia, what would we see?’ Necib’s collaborator, Bryan Ostdiek (formerly at University of Oregon, and now at Harvard University), who had previously been involved in the Large Hadron Collider (LHC) project, had experience dealing with huge datasets using machine and deep learning. Porting those methods over to astrophysics opened the door to a new way to explore the cosmos.
“At the LHC, we have incredible simulations, but we worry that machines trained on them may learn the simulation and not real physics,” Ostdiek said. “In a similar way, the FIRE galaxies provide a wonderful environment to train our models, but they are not the Milky Way. We had to learn not only what could help us identify the interesting stars in simulation, but also how to get this to generalize to our real galaxy.”
The team developed a method of tracking the movements of each star in the virtual galaxies and labeling the stars as either born in the host galaxy or accreted as the products of galaxy mergers. The two types of stars have different signatures, though the differences are often subtle. These labels were used to train the deep learning model, which was then tested on other FIRE simulations.
After they built the catalog, they applied it to the Gaia data. “We asked the neural network, based on what you’ve learned, can you label if the stars were accreted or not?” Necib said. The model ranked how confident it was that a star was born outside the Milky Way on a range from 0 to 1. The team created a cutoff with a tolerance for error and began exploring the results.
This approach of applying a model trained on one dataset and applying it to a different but related one is called transfer learning and can be fraught with challenges. “We needed to make sure that we’re not learning artificial things about the simulation, but really what’s going on in the data,” Necib said. “For that, we had to give it a little bit of help and tell it to reweigh certain known elements to give it a bit of an anchor.”
Unveiling the Gaia Sausage
They first checked to see if it could identify known features of the galaxy. These include “the Gaia sausage”—an ancient and dramatic head-on collision between the Milky Way and a smaller object, dubbed the “Sausage” galaxy that was a defining event in the early history of the Milky Way that reshaped the structure of our galaxy, fashioning both its inner bulge and its outer halo.
“It has a very specific signature,” she explained. “If the neural network worked the way it’s supposed to, we should see this huge structure that we already know is there.”
“The collision ripped the dwarf to shreds, leaving its stars moving in very radial orbits” that are long and narrow like needles, said Vasily Belokurov of the University of Cambridge and the Center for Computational Astrophysics at the Flatiron Institute in New York City who was not involved in the study. The stars’ paths take them “very close to the center of our galaxy. This is a telltale sign that the dwarf galaxy came in on a really eccentric orbit and its fate was sealed.”
The new research also identified at least eight large, spherical clumps of stars called globular clusters that were brought into the Milky Way by the Sausage galaxy. Small galaxies generally do not have globular clusters of their own, so the Sausage galaxy must have been big enough to host a collection of clusters.
“While there have been many dwarf satellites falling onto the Milky Way over its life, this was the largest of them all,” said Sergey Koposov of Carnegie Mellon University, not part of the Nyx discovery, who has studied the kinematics of the Sausage stars and globular clusters in detail about at least eight large, spherical clumps of stars called globular clusters that were brought into the Milky Way by the Sausage galaxy. Small galaxies generally do not have globular clusters of their own, so the Sausage galaxy must have been big enough to host a collection of clusters..
The Gaia sausage was there, as was the stellar halo—background stars that give the Milky Way its tell-tale shape—and the Helmi stream, another known dwarf galaxy that merged with the Milky Way in the distant past and was discovered in 1999.
The Nyx model identified another structure in the analysis: a cluster of 250 stars, rotating with the Milky Way’s disk, but also going toward the center of the galaxy. “Your first instinct is that you have a bug,” Necib recounted. “And you’re like, ‘Oh no!’ So, I didn’t tell any of my collaborators for three weeks. Then I started realizing it’s not a bug, it’s actually real and it’s new.”
But what if it had already been discovered? “You start going through the literature, making sure that nobody has seen it and luckily for me, nobody had. So I got to name it, which is the most exciting thing in astrophysics. I called it Nyx. This particular structure is very interesting because it would have been very difficult to see without machine learning.”
The project required advanced computing at many different stages. The FIRE and updated FIRE-2 simulations are among the largest computer models of galaxies ever attempted. Each of the nine main simulations—three separate galaxy formations, each with slightly different starting point for the sun—took months to compute on the largest, fastest supercomputers in the world. These included Blue Waters at the National Center for Supercomputing Applications (NCSA), NASA’s High-End Computing facilities, and most recently Stampede2 at the Texas Advanced Computing Center (TACC).
“Everything about this project is computationally very intensive and would not be able to happen without large-scale computing,” Necib said.
Necib and her team plan to explore Nyx further using ground-based telescopes. This will provide information about the chemical makeup of the stream, and other details that will help them date Nyx’s arrival into the Milky Way, and possibly provide clues on where it came from.
The next data release of Gaia in 2021 will contain additional information about 100 million stars in the catalog, making more discoveries of accreted clusters likely.
More information: Lina Necib et al, Evidence for a vast prograde stellar stream in the solar vicinity, Nature Astronomy (2020). DOI: 10.1038/s41550-020-1131-2
Image credit: The Real Milky Way –“Our Warped, Twisted, Wobbly Galaxy”