The discovery forms part of a treasure trove of data made available in Gaia’s Data Release 3, which provides the most detailed survey of our galactic neighbourhood to date.

Observations made by the Gaia observatory, which orbits a point in space about 1.5 million km from Earth, will allow astronomers to create the most accurate and complete multi-dimensional map of the Milky Way and better understand our place in the Universe.

Martin Barstow, Professor of Astrophysics and Space Science at the University of Leicester and Director of Strategic Partnerships for Space Park Leicester, is part of the Gaia collaboration to have co-authored multiple papers using the new data.

Other highlights of Data Release 3 include description of ‘starquakes’, stellar DNA and a new binary star catalogue of more than 800,000 binary systems. Before Gaia launched in 2013, only around 30,000 binaries were known in our galaxy.

Professor Barstow said:

“Gaia data has been moving through astronomy like a tidal wave. It’s the most productive observatory we have available to use right now, and it’s transforming both astronomy and our understanding of the Universe.

“This Data Release 3 is a complete step change. All the data we have catalogued using spectra — stellar distances, ages, composition, and more — adds an extra dimension to what we know about the stars in our galaxy, and represents a huge leap forward.”

Data reveals a ‘super Jupiter’ companion

Observations of exoplanets orbiting white dwarfs is notoriously difficult. White dwarfs are the core remnant of stars not massive enough to become a black hole or neutron star.

However, by analysing the motion of the metal-rich white dwarf WD 0141-675 and noting a ‘wobble’ in its orbit, researchers inferred the existence of a companion object with a mass around nine times that of Jupiter. Too small to be a star, this must be an exoplanet.

This ‘super Jupiter’ is only the third known exoplanet to orbit a white dwarf, and makes WD 0141-675 the closest white dwarf to Earth to host a planet.

Stars in their eyes: Gaia more than doubles number of known binaries

Gaia’s Data Release 3 also expands astronomers’ understanding of binary systems, where two stars are gravitationally bound to one another. Sirius, the brightest star visible from Earth (after our Sun), is a binary system comprising a main sequence star, Sirius A, and a faint white dwarf companion, Sirius B.

These new datasets both refine the stellar catalogue of known binaries and add many more new such systems, bringing the total of known binary systems from around 300,000 before Data Release 3 to more than 800,000. Researchers detect binaries using Gaia’s radial velocity spectrometer and a variety of techniques;

• astrometry: by detecting the motion of source objects which are not uniform and are observed to ‘wobble’ or otherwise change direction from what would otherwise be expected;
• photometry: when aligned with Gaia’s line of sight, where one star is observed to pass in front of another and periodically ‘eclipse’ its companion;
• and spectroscopy: these binaries have a radial velocity that varies periodically, depending on whether a star approaches or recedes from our viewpoint at Earth.

Professor Barstow continued:

“We have so much more data on binaries with this release and, crucially, that data is so much more precise than what has gone before.

“Once you have more precise data about a binary system you can work out all sorts of things such as ages and composition: all aspects we haven’t had information on before.

“And by answering those questions, we can start to understand more of the fundamentals about how our Universe works, including how stars live and die.”

HD 53143 — a roughly billion-year-old Sun-like star located 59.8 light-years from Earth in the Carina constellation — was first observed with the coronagraphic Advanced Camera for Surveys on the Hubble Space Telescope (HST) in 2006. It also is surrounded by a debris disk — a belt of comets orbiting a star that are constantly colliding and grinding down into smaller dust and debris — that scientists previously believed to be a face-on ring similar to the debris disk surrounding our Sun, more commonly known as the Kuiper Belt.

The new observations were made of HD 53143 using the highly-sensitive Band 6 receivers on ALMA, an observatory co-operated by the U.S. National Science Foundation’s National Radio Astronomy Observatory (NRAO), and have revealed that the star system’s debris disk is actually highly eccentric. In ring-shaped debris disks, the star is typically located at or near the center of the disk. But in elliptically-shaped eccentric disks, the star resides at one focus of the ellipse, far away from the disk’s center. Such is the case with HD 53143, which wasn’t seen in previous coronagraphic studies because coronagraphs purposely block the light of a star in order to more clearly see nearby objects. The star system may also be harboring a second disk and at least one planet.

“Until now, scientists had never seen a debris disk with such a complicated structure. In addition to being an ellipse with a star at one focus, it also likely has a second inner disk that is misaligned or tilted relative to the outer disk,” said Meredith MacGregor, an assistant professor at the Center for Astrophysics and Space Astronomy (CASA) and Department of Astrophysical and Planetary Sciences (APS) at CU Boulder, and the lead author on the study. “In order to produce this structure, there must be a planet or planets in the system that are gravitationally perturbing the material in the disk.”

This level of eccentricity, MacGregor said, makes HD 53143 the most eccentric debris disk observed to date, being twice as eccentric as the Fomalhaut debris disk, which MacGregor fully imaged at millimeter wavelengths using ALMA in 2017. “So far, we have not found many disks with a significant eccentricity. In general, we don’t expect disks to be very eccentric unless something, like a planet, is sculpting them and forcing them to be eccentric. Without that force, orbits tend to circularize, like what we see in our own Solar System.”

Importantly, MacGregor notes that debris disks aren’t just collections of dust and rocks in space. They are a historical record of planetary formation and how planetary systems evolve over time. and provide a peek into their futures. “We can’t study the formation of Earth and the Solar System directly, but we can study other systems that appear similar to but younger than our own. It’s a bit like looking back in time,” she said. “Debris disks are the fossil record of planet formation, and this new result is confirmation that there is much more to be learned from these systems and that knowledge may provide a glimpse into the complicated dynamics of young star systems similar to our own Solar System.”

Dr. Joe Pesce, NSF program officer for ALMA, added, “We are finding planets everywhere we look, and these fabulous results by ALMA are showing us how planets form — both those around other stars and in our own Solar System. This research demonstrates how astronomy works and how progress is made, informing not only what we know about the field but also about ourselves.”

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“All of the hard problems in AI really are judgment and data problems, and the interesting thing about that is when you start thinking about war, the hard problems are strategy and uncertainty, or what is well known as the fog of war,” said Jon Lindsay, an associate professor in the School of Cybersecurity & Privacy and the Sam Nunn School of International Affairs. “You need human sense-making and to make moral, ethical, and intellectual decisions in an incredibly confusing, fraught, scary situation.”

AI decision-making is based on four key components: data about a situation, interpretation of those data (or prediction), determining the best way to act in line with goals and values (or judgment), and action. Machine learning advancements have made predictions easier, which makes data and judgment even more valuable. Although AI can automate everything from commerce to transit, judgment is where humans must intervene, Lindsay and University of Toronto Professor Avi Goldfarb wrote in the paper, “Prediction and Judgment: Why Artificial Intelligence Increases the Importance of Humans in War,” published in International Security.

Many policy makers assume human soldiers could be replaced with automated systems, ideally making militaries less dependent on human labor and more effective on the battlefield. This is called the substitution theory of AI, but Lindsay and Goldfarb state that AI should not be seen as a substitute, but rather a complement to existing human strategy.

“Machines are good at prediction, but they depend on data and judgment, and the most difficult problems in war are information and strategy,” he said. “The conditions that make AI work in commerce are the conditions that are hardest to meet in a military environment because of its unpredictability.”

An example Lindsay and Goldfarb highlight is the Rio Tinto mining company, which uses self-driving trucks to transport materials, reducing costs and risks to human drivers. There are abundant, predictable, and unbiased data traffic patterns and maps that require little human intervention unless there are road closures or obstacles.

War, however, usually lacks abundant unbiased data, and judgments about objectives and values are inherently controversial, but that doesn’t mean it’s impossible. The researchers argue AI would be best employed in bureaucratically stabilized environments on a task-by-task basis.

“All the excitement and the fear are about killer robots and lethal vehicles, but the worst case for military AI in practice is going to be the classically militaristic problems where you’re really dependent on creativity and interpretation,” Lindsay said. “But what we should be looking at is personnel systems, administration, logistics, and repairs.”

There are also consequences to using AI for both the military and its adversaries, according to the researchers. If humans are the central element to deciding when to use AI in warfare, then military leadership structure and hierarchies could change based on the person in charge of designing and cleaning data systems and making policy decisions. This also means adversaries will aim to compromise both data and judgment since they would largely affect the trajectory of the war. Competing against AI may push adversaries to manipulate or disrupt data to make sound judgment even harder. In effect, human intervention will be even more necessary.

Yet this is just the start of the argument and innovations.

“If AI is automating prediction, that’s making judgment and data really important,” Lindsay said. “We’ve already automated a lot of military action with mechanized forces and precision weapons, then we automated data collection with intelligence satellites and sensors, and now we’re automating prediction with AI. So, when are we going to automate judgment, or are there components of judgment cannot be automated?”

Until then, though, tactical and strategic decision making by humans continues to be the most important aspect of warfare.

“Writing quantum algorithms is radically different from writing classical computing programs and requires some understanding of quantum principles and the mathematics behind them,” said Andrey Y. Lokhov, a scientist at Los Alamos National Laboratory and lead author of the recently published guide in ACM Transactions on Quantum Computing. “Our guide helps quantum programmers get started in the field, which is bound to grow as more and more quantum computers with more and more qubits become commonplace.”

In succinct, stand-alone sections, the guide surveys 20 quantum algorithms — including famous, foundational quantum algorithms, such as Grover’s Algorithm for database searching and much more, and Shor’s Algorithm for factoring integers. Making the real-world connection, the guide then walks programmers through implementing the algorithms on IBM’s publicly available 5-qubit IBMQX4 quantum computer and others. In each case, the authors discuss the results of the implementation and explain differences between the simulator and the actual hardware runs.

“This article was the result of a rapid-response effort by the Information Science and Technology Institute at Los Alamos, where about 20 Lab staff members self-selected to learn about and implement a standard quantum algorithm on the IBM Q quantum system,” said Stephan Eidenbenz, a senior quantum computing scientist at Los Alamos, a coauthor of the article and director of ISTI when work on it began.

The goal was to prepare the Los Alamos workforce for the quantum era by guiding those staff members with little or no quantum computing experience all the way through implementation of a quantum algorithm on a real-life quantum computer, Eidenbenz said.

These staff members, in addition to a few students and well-established quantum experts, make up the long author list of this “crowd-sourced” overview article that has already been heavily cited, Eidenbenz said.

The first section of the guide covers the basics of quantum computer programming, explaining qubits and qubit systems, fundamental quantum concepts of superposition and entanglement and quantum measurements before tackling the deeper material of unitary transformations and gates, quantum circuits and quantum algorithms.

The section on the IBM quantum computer covers the set of gates available for algorithms, the actual physical gates implemented, how the qubits are connected and the sources of noise, or errors.

Another section looks at the various types of quantum algorithms. From there, the guide dives into the 20 selected algorithms, with a problem definition, description and steps for implementing each one on the IBM or, in a few cases, other computers.

Extensive references at the end of the guide will help interested readers go deeper in their explorations of quantum algorithms.

Information Science and Technology Institute at Los Alamos National Laboratory through the Laboratory Directed Research and Development program.

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Made up of swaths of sediment surrounding the river, channel belts, once hardened into rock, preserve the paths of rivers that once were. However, reconstructing details about an ancient river from channel belt deposits is a notoriously difficult task.

New research from scientists at The University of Texas at Austin is making progress on that front. Lead author Tian Dong, a postdoctoral researcher at the UT Jackson School of Geosciences, said that by analyzing modern rivers they have been able to come up with a rule that connects channel belts to river patterns, finding that, in general, the more channels a river has, the narrower its channel belt.

Since the physics shaping rivers is the same over time and place, the rule should hold for ancient rivers and rivers on other planets, too, according to co-author Timothy Goudge, an assistant professor at the Jackson School.

“We can look at a river deposit from 100 million years ago on Earth or from 3.5 billion years ago on Mars and we can say something about what the actual river looked like,” he said.

The results were published on June 13 in the journal Geology.

In addition to helping scientists envision ancient rivers, the rule can also help them interpret how these rivers influenced the broader landscape. Rivers with narrower channel belts are able to more readily access the surrounding floodplain — which shapes how landscapes are built up and material is deposited downstream.

“For multichannel systems, such as braided rivers, they actually sit in a very narrow channel belt so they’re very close to the floodplain,” said Dong. “So potentially there’s more interaction between the river and floodplain material.”

The rule does have some caveats. It does not hold for confined rivers that are kept from freely migrating by their surrounding landscape. But when rivers are free to move and meander across the land there is a direct connection between an increasing number of river channels and a narrowing channel belt. The scientists also found that as the belt narrows, it also becomes smoother with less sharp edges.

The researchers discovered the rule by analyzing 30 modern rivers and their channel belts, drawing on high-resolution images and elevation data captured by satellites. Dong said that he had a hunch about the connection, noticing a trend between river channel and channel belt width from scrolling around on Google Earth. But he wasn’t sure if his intuition would prove correct once the data was computed.

“No one had really looked at the relationship between river and channel-belt planform shapes systematically, so we didn’t really know what we’d expect,” Dong said.

In addition to having narrower channel belts, the research also found that multichannel rivers take up more space on the channel belt, taking up 50% or more of the channel belt area. In contrast, single-channel systems, like meandering rivers, take up as little as 1%. This further boosts the ability of multichannel rivers to pick up and move sediments, Dong said. Since organic matter from plants and animals is among those sediments, it means that multichannel rivers may not store organic carbon on their floodplains as long before transporting it into the ocean — where it can affect sea life.

Channel belts are a common feature on Mars, serving as a reminder of the Red Planet’s wetter past. They are also probably found on Saturn’s moon Titan, where rivers of liquid methane have been identified by space probes.

Both Goudge and Dong said they hope to apply their research on rivers to learn about the geology that shapes other worlds.

“For future work, we will be looking to apply these metrics to other planets in our solar system and see what we can see,” Goudge said.

The research was funded by a Postdoctoral Fellowship award to Dong from the National Science Foundation.