Astronomers Discover Widest Separation of Brown Dwarf Pair to Date

An artist’s rendition of a binary system of brown dwarfs like cwise j014611.20-050850.0ab.
Credit: William Pendrill

Maunakea, Hawaiʻi – A team of astronomers has discovered a rare pair of brown dwarfs that has the widest separation of any brown dwarf binary system found to date.

“Because of their small size, brown dwarf binary systems are usually very close together,” said Emma Softich, an undergraduate astrophysics student at the Arizona State University (ASU) School of Earth and Space Exploration and lead author of the study. “Finding such a widely separated pair is very exciting.”

The gravitational force between a pair of brown dwarfs is lower than for a pair of stars with the same separation, so wide brown dwarf binaries are more likely to break up over time, making this pair of brown dwarfs an exceptional find.

The study, which is based on observations the University of California San Diego (UC San Diego) Cool Star Lab conducted with W. M. Keck Observatory on Maunakea, Hawaiʻi Island, is published in today’s issue of The Astrophysical Journal Letters.                           

Using Keck Observatory’s Near-Infrared Echellette Spectrometer, or NIRES instrument,  members of the UC San Diego Cool Star Lab, including Physics Professor Adam Burgasser and graduate students Christian Aganze and Dino Hsu, obtained infrared spectra of the brown dwarf binary system, called CWISE J014611.20-050850.0AB. The data revealed the two brown dwarfs are about 12 billion miles apart, or three times the separation of Pluto from the Sun. This distance confirms the unusual brown dwarf couple breaks the record for having the widest separation from each other.

“Keck’s exceptional sensitivity in the infrared with this instrument was critical for our measurements,” said co-author Burgasser, who leads the Cool Star Lab. “The secondary brown dwarf of this system is exceptionally faint, but with Keck we were able to obtain good enough spectral data to classify both sources and identify them as members of a rare class of blue L dwarfs.”

“Wide, low-mass systems like CWISE J014611.20-050850.0AB are usually disrupted early on in their lifetimes, so the fact that this one has survived until now is pretty remarkable,” said co-author Adam Schneider of the U.S. Naval Observatory, Flagstaff Station and George Mason University.

WISE (left) and the Dark Energy Survey Collaboration (DES) (right) images of CWISE J0146-0508AB. In the lower-resolution WISE image, the pair are blended into a single point-source, while two distinct entities are visible in the higher-resolution DES image. The reddish hue of both objects in the DES image shows that they emit much of their light in the infrared, a trait typical of brown dwarfs. Credit: WISE/DES/Softich et al.

Brown dwarfs are celestial objects that are smaller than a normal star. These objects are not massive enough to sustain nuclear fusion and shine like normal stars, but are hot enough to radiate energy.

Many brown dwarfs have been discovered with data from NASA’s Wide-field Infrared Survey Explorer (WISE) via the Backyard Worlds: Planet 9 citizen science project, which solicits help from the public to search the WISE image data bank to find brown dwarfs and low-mass stars, some of the Sun’s nearest neighbors.

For this study, the researchers inspected images of Backyard Worlds discoveries, where companion brown dwarfs may have been overlooked. In doing so, they discovered the rare CWISE J014611.20 050850.0AB brown dwarf binary system.

Softich went through about 3,000 brown dwarfs from Backyard Worlds one by one and compared the WISE images to other survey images, looking for evidence of a brown dwarf companion to the original target. The team then used data from the Dark Energy Survey (DES) to confirm that it was indeed a brown dwarf pair.

They then used Keck Observatory’s NIRES to confirm the brown dwarfs have spectral types L4 and L8, and that they are at an estimated distance of about 40 parsecs, or 130.4 light-years from Earth, with a projected separation of 129 astronomical units, or 129 times the distance between the Sun and the Earth.

The team hopes this discovery will allow astronomers the chance to study brown dwarf binary systems and to develop models and procedures that will help in recognizing more of them in the future.

“Binary systems are used to calibrate many relations in astronomy, and this newly discovered pair of brown dwarfs will present an important test of brown dwarf formation and evolution models,” said co-author Jennifer Patience, Softich’s adviser at ASU.

Source: W. M. Keck Observatory

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About NIRES

The Near Infrared Echellette Spectrograph (NIRES) is a prism cross-dispersed near-infrared spectrograph built at the California Institute of Technology by a team led by Chief Instrument Scientist Keith Matthews and Prof. Tom Soifer. Commissioned in 2018, NIRES covers a large wavelength range at moderate spectral resolution for use on the Keck II telescope and observes extremely faint red objects found with the Spitzer and WISE infrared space telescopes, as well as brown dwarfs, high-redshift galaxies, and quasars. Support for this technology was generously provided by the Mt. Cuba Astronomical Foundation.

About W. M. Keck Observatory

The W. M. Keck Observatory telescopes are among the most scientifically productive on Earth. The two 10-meter optical/infrared telescopes atop Maunakea on the Island of Hawaiʻi feature a suite of advanced instruments including imagers, multi-object spectrographs, high-resolution spectrographs, integral-field spectrometers, and world-leading laser guide star adaptive optics systems. Some of the data presented herein were obtained at Keck Observatory, which is a private 501(c) 3 non-profit organization operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the Native Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.

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27 million galaxy morphologies quantified and cataloged with the help of machine learning

An image of NGC 1365 collected by the Dark Energy Survey. Also known as the Great Barred Spiral Galaxy, NGC 1365 is an example of a spiral galaxy and is located about 56 million light-years away. (Image: DECam, DES Collaboration) 

Using data from the Dark Energy Survey, researchers from the Department of Physics & Astronomy produced the largest catalog of galaxy morphology classifications to date. 

Research from Penn’s Department of Physics and Astronomy has produced the largest catalog of galaxy morphology classification to date. Led by former postdocs Jesús Vega-Ferrero and Helena Domínguez Sánchez, who worked with professor Mariangela Bernardi, this catalog of 27 million galaxy morphologies provides key insights into the evolution of the universe. The study was published in Monthly Notices of the Royal Astronomical Society.

The researchers used data from the Dark Energy Survey (DES), an international research program whose goal is to image one-eighth of the sky to better understand dark energy’s role in the accelerating expansion of the universe.

A byproduct of this survey is that the DES data contains many more images of distant galaxies than other surveys to date. “The DES images show us what galaxies looked like more than 6 billion years ago,” says Bernardi.

And because DES has millions of high-quality images of astronomical objects, it’s the perfect dataset for studying galaxy morphology. “Galaxy morphology is one of the key aspects of galaxy evolution. The shape and structure of galaxies has a lot of information about the way they were formed, and knowing their morphologies gives us clues as to the likely pathways for the formation of the galaxies,” Domínguez Sánchez says.

Previously, the researchers had published a morphological catalog for more than 600,000 galaxies from the Sloan Digital Sky Survey (SDSS). To do this, they developed a convolutional neural network, a type of machine learning algorithm, that was able to automatically categorize whether a galaxy belonged to one of two major groups: spiral galaxies, which have a rotating disk where new stars are born, and elliptical galaxies, which are larger, and made of older stars which move more randomly than their spiral counterparts.

But the catalog developed using the SDSS dataset was primarily made of bright, nearby galaxies, says Vega-Ferrero. In their latest study, the researchers wanted to refine their neural network model to be able to classify fainter, more distant galaxies. “We wanted to push the limits of morphological classification and trying to go beyond, to fainter objects or objects that are farther away,” Vega-Ferrero says.

To do this, the researchers first had to train their neural network model to be able to classify the more pixelated images from the DES dataset. They first created a training model with previously known morphological classifications, comprised of a set of 20,000 galaxies that overlapped between DES and SDSS. Then, they created simulated versions of new galaxies, mimicking what the images would look like if they were farther away using code developed by staff scientist Mike Jarvis.

Images of a simulated spiral (top) and elliptical galaxy at varying image quality and redshift levels, illustrating how fainter and more distant galaxies might look within the DES dataset. (Image: Jesus Vega-Ferrero and Helena Dominguez-Sanchez).  

Once the model was trained and validated on both simulated and real galaxies, it was applied to the DES dataset, and the resulting catalog of 27 million galaxies includes information on the probability of an individual galaxy being elliptical or spiral. The researchers also found that their neural network was 97% accurate at classifying galaxy morphology, even for galaxies that were too faint to classify by eye.

“We pushed the limits by three orders of magnitude, to objects that are 1,000 times fainter than the original ones,” Vega-Ferrero says. “That is why we were able to include so many more galaxies in the catalog.”

“Catalogs like this are important for studying galaxy formation,” Bernardi says about the significance of this latest publication. “This catalog will also be useful to see if the morphology and stellar populations tell similar stories about how galaxies formed.”

For the latter point, Domínguez Sánchez is currently combining their morphological estimates with measures of the chemical composition, age, star-formation rate, mass, and distance of the same galaxies. Incorporating this information will allow the researchers to better study the relationship between galaxy morphology and star formation, work that will be crucial for a deeper understanding of galaxy evolution.

Bernardi says that there are a number of open questions about galaxy evolution that both this new catalog, and the methods developed to create it, can help address. The upcoming LSST/Rubin survey, for example, will use similar photometry methods to DES but will have the capability of imaging even more distant objects, providing an opportunity to gain even deeper understanding of the evolution of the universe.

Mariangela Bernardi is a professor in the Department of Physics and Astronomy in the School of Arts & Sciences at the University of Pennsylvania.

Helena Domínguez Sánchez is a former Penn postdoc and is currently a postdoctoral fellow at Instituto de Ciencias del Espacio (ICE), which is part of the Consejo Superior de Investigaciones Científicas (CSIC).

Jesús Vega Ferrero is a former Penn postdoc and currently a postdoctoral researcher at the Instituto de Física de Cantabria (IFCA), which is part of the Consejo Superior de Investigaciones Científicas (CSIC).

The Dark Energy Survey is supported by funding from the Department of Energy’s Fermi National Accelerator Laboratory, the National Center for Supercomputing Applications, and the National Science Foundation’s NOIRLab. A complete list of funding organizations and collaborating institutions is at The Dark Energy Survey website.

This research was supported by NSF Grant AST-1816330.

 

Source: Penn Today, University of Pennsylvania

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Dark Energy Survey physicists open new window into dark energy

A map of the sky showing the density of galaxy clusters, galaxies and matter in the universe over the part of the sky observed by the Dark Energy Survey. The left panel shows the galaxy density in that part of the sky, while the middle panel shows matter density and the right shows galaxy cluster density. Red areas are more dense, and blue areas are less dense, than average. (Chun-Hao To/Stanford University, SLAC)

For the first time, DES scientists can combine measurements of the distribution of matter, galaxies, and galaxy clusters to advance our understanding of dark energy. 

The universe is expanding at an ever-increasing rate, and while no one is sure why, researchers with the Dark Energy Survey (DES) at least had a strategy for figuring it out: They would combine measurements of the distribution of matter, galaxies and galaxy clusters to better understand what’s going on.

Reaching that goal turned out to be pretty tricky, but now a team led by researchers at the Department of Energy’s SLAC National Accelerator Laboratory, Stanford University and the University of Arizona have come up with a solution. Their analysis, published today in Physical Review Letters, yields more precise estimates of the average density of matter as well as its propensity to clump together – two key parameters that help physicists probe the nature of dark matter and dark energy, the mysterious substances that make up the vast majority of the universe.

“It is one of the best constraints from one of the best data sets to date,” says Chun-Hao To, a lead author on the new paper and a graduate student at SLAC and Stanford working with Kavli Institute for Particle Astrophysics and Cosmology Director Risa Wechsler. 

An early goal

When DES set out in 2013 to map an eighth of the sky, the goal was to gather four kinds of data: the distances to certain types of supernovae, or exploding stars; the distribution of matter in the universe; the distribution of galaxies; and the distribution of galaxy clusters. Each tells researchers something about how the universe has evolved over time. 

Ideally, scientists would put all four data sources together to improve their estimates, but there’s a snag: The distributions of matter, galaxies, and galaxy clusters are all closely related. If researchers don’t take these relationships into account, they will end up “double counting,” placing too much weight on some data and not enough on others, To says.

To avoid mishandling all this information, To, University of Arizona astrophysicist Elisabeth Krause and colleagues have developed a new model that could properly account for the connections in the distributions of all three quantities: matter, galaxies, and galaxy clusters. In doing so, they were able to produce the first-ever analysis to properly combine all these disparate data sets in order to learn about dark matter and dark energy.

Improving estimates

Adding that model into the DES analysis has two effects, To says. First, measurements of the distributions of matter, galaxies and galaxy clusters tend to introduce different kinds of errors. Combining all three measurements makes it easier to identify any such errors, making the analysis more robust. Second, the three measurements differ in how sensitive they are to the average density of matter and its clumpiness.  As a result, combining all three can improve the precision with which the DES can measure dark matter and dark energy.

In the new paper, To, Krause and colleagues applied their new methods to the first year of DES data and sharpened the precision of previous estimates for matter’s density and clumpiness.

Now that the team can incorporate matter, galaxies and galaxy clusters simultaneously in their analysis, adding in supernova data will be relatively straightforward, since that kind of data is not as closely related with the other three, To says.

“The immediate next step,” he says, “is to apply the machinery to DES Year 3 data, which has three times larger coverage of the sky.” This is not as simple as it sounds: While the basic idea is the same, the new data will require additional efforts to improve the model to keep up with the higher quality of the newer data, To says.

“This analysis is really exciting,” Wechsler said. “I expect it to set a new standard in the way we are able to analyze data and learn about dark energy from large surveys, not only for DES but also looking forward to the incredible data that we will get from the Vera Rubin Observatory’s Legacy Survey of Space and Time in a few years.”

The research was a collaborative effort within the Dark Energy Survey and was supported by the National Science Foundation and the Department of Energy's Office of Science. 

Citation: C. To et al. (DES Collaboration), Physical Review Letters, 6 April 2021 (10.1103/PhysRevLett.126.141301)

For questions or comments, contact the SLAC Office of Communications at communications@slac.stanford.edu.

By Nathan Collins

Source:  National Accelerator Laboratory (SLAC)/News

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