A star is being consumed by a nearby supermassive black hole. Astronomers call this a tidal disruption event (TDE). What makes this TDE a very rare TDE is that as the black hole rips apart the star, two jets of material moving with almost the speed of light are launched in opposite directions producing light in all wavelengths. Such jetted-TDEs are extremely rare and AT2022cmc, depicted here is the first one discovered with an optical telescope. Image credit: Carl Knox (OzGrav, ARC Centre of Excellence for Gravitational Wave Discovery, Swinburne University of Technology)
A star is being consumed by a nearby supermassive black hole. Astronomers call this a tidal disruption event (TDE). What makes this TDE a very rare TDE is that as the black hole rips apart the star, two jets of material moving with almost the speed of light are launched in opposite directions producing light in all wavelengths. Such jetted-TDEs are extremely rare and AT2022cmc, depicted here is the first one discovered with an optical telescope. Image credit: Carl Knox (OzGrav, ARC Centre of Excellence for Gravitational Wave Discovery, Swinburne University of Technology)
Tyler O'Neal, Staff Editor BIG DATA

Caltech astronomers use big data mining to identify rare cosmic events in ZTF survey data 

Astronomers from the Zwicky Transient Facility collaboration have observed a truly rare cosmic “lunch” - a supermassive black hole devouring a nearby star and releasing powerful jets in the process. Dubbed AT2022cmc, this discovery was made while sieving through ZTF survey data using a novel method designed to alert astronomers for such rare events in near real-time.

The universe can be a violent place. Stars die or collide with each other and black holes devour everything that gets too close. These and other events produce flashes of light in the night sky that astronomers call transients. The Zwicky Transient Facility is currently one of the largest transient surveys astronomers use to study the ever-changing universe. The survey is also a treasure trove of rare, strange, and unusual events that often astronomers discover by chance.

“Our new search technique helps us to quickly identify rare cosmic events in the ZTF survey data. And since ZTF and upcoming larger surveys such as Vera Rubin’s LSST scan the sky so frequently, we can now expect to uncover a wealth of rare, or previously undiscovered cosmic events and study them in detail,” says Igor Andreoni, a postdoctoral associate in the Department of Astronomy at UMD and NASA Goddard Space Flight Center.

AT2022cmc is a peculiar case of what is known as a tidal-disruption event or TDE. TDEs happen with a star approaching a black hole is violently ripped apart by the black hole’s gravitational tidal forces—similar to how the Moon pulls tides on Earth but with greater strength. Then, pieces of the star are captured into a swiftly spinning disk orbiting the black hole. Finally, the black hole consumes what remains of the doomed star in the disk.

In some extremely rare cases such as AT2022cmc, the supermassive black hole launches “relativistic jets”—beams of matter traveling close to the speed of light—after destroying a star. Discovered in Feb 2022, astronomers led by Andreoni followed up AT2022cmc and observed it with multiple facilities at multiple wavelengths. 

“The last time scientists discovered one of these jets was well over a decade ago,” said Michael Coughlin, an assistant professor of astronomy at the University of Minnesota Twin Cities and co-lead on the paper. “From the data we have, we can estimate that relativistic jets are launched in only 1% of these destructive events, making AT2022cmc an extremely rare occurrence. In fact, the luminous flash from the event is among the brightest ever observed.”

The novel data-crunching method - equivalent to searching through a million pages of information every night - allowed Andreoni and colleagues to conduct a rapid analysis of the ZTF data and identify the AT2022cmc TDE with relativistic jets. They quickly started follow-up observations that revealed an exceptionally bright event across the electromagnetic spectrum, from the X-rays to the millimeter and radio.

ESO’s Very Large Telescope revealed that AT2022cmc was at a cosmological distance of 8.5 billion light years away. The Hubble Space Telescope optical/infrared images and radio observations from the Very Large Array pinpointed the location of AT2022cmc with extreme precision.

The researchers believe that AT2022cmc was at the center of a galaxy that is not yet visible because the light from AT2022cmc outshone it, but future space observations with Hubble or James Webb Space Telescopes may unveil the galaxy when the transient eventually disappears.

It is still a mystery why some TDEs launch jets while others may not. From their observations, Andreoni and his team concluded that the black holes in AT2022cmc and other similarly jetted TDEs are likely spinning rapidly so as to power the extremely luminous jets. This suggests that a rapid black hole spin may be one necessary ingredient for jet launching—an idea that brings researchers closer to understanding the physics of supermassive black holes at the center of galaxies billions of light years away.

Before AT2022cmc, only a couple of possible jetted TDEs were known, primarily discovered by gamma-ray space missions, which detect the highest-energy forms of radiation produced by these jets. With their new method, astronomers can now search for such rare events in ground-based optical surveys.

“Astronomy is changing rapidly,” Andreoni said. “More optical and infrared all-sky surveys are now active or will soon come online. Scientists can use AT2022cmc as a model for what to look for and find more disruptive events from distant black holes. This means that more than ever, big data mining is an important tool to advance our knowledge of the universe.”

Cambridge-built AI tackles the challenge of materials structure prediction

Tyler O'Neal, Staff Editor BIG DATA

Researchers have designed a machine learning method that can predict the structure of new materials with five times the efficiency of the current standard, removing a key roadblock in developing advanced materials for applications such as energy storage and photovoltaics.

University of Cambridge researchers have designed a way to predict the structure of materials given their constitutive elements. The results are reported in the journal Science Advances. gettyimages 1148243894 24ca8

The arrangement of atoms in a material determines its properties. The ability to predict this arrangement computationally for different combinations of elements, without having to make the material in the lab, would enable researchers to quickly design and improve materials. This paves the way for advances such as better batteries and photovoltaics.

However, there are many ways that atoms can ‘pack’ into a material: some packings are stable, others are not. Determining the stability of packing is computationally intensive, and calculating every possible arrangement of atoms to find the best one is not practical. This is a significant bottleneck in materials science.

“This materials structure prediction challenge is similar to the protein folding problem in biology,” said Dr. Alpha Lee from Cambridge’s Cavendish Laboratory, who co-led the research. “There are many possible structures that a material can ‘fold’ into. Except the materials science problem is perhaps even more challenging than biology because it considers a much broader set of elements.”

Lee and his colleagues developed a method based on machine learning that successfully tackles this challenge. They developed a new way to describe materials, using the mathematics of symmetry to reduce the infinite ways that atoms can pack into materials into a finite set of possibilities. They then used machine learning to predict the ideal packing of atoms, given the elements and their relative composition in the material.

Their method accurately predicts the structure of materials that hold promise for piezoelectric and energy harvesting applications, with over five times the efficiency of current methods. Their method can also find thousands of new and stable materials that have never been made before, in a way that is super computationally efficient.  

“The number of materials that are possible is four to five orders of magnitude larger than the total number of materials that we have made since antiquity,” said co-first author Dr. Rhys Goodall, also from the Cavendish Laboratory. “Our approach provides an efficient computational approach that can ‘mine’ new stable materials that have never been made before. These hypothetical materials can then be computationally screened for their functional properties.”

The researchers are now using their machine learning platform to find new functional materials such as dielectric materials. They are also integrating other aspects of experimental constraints into their materials discovery approach.

Japanese researchers predict the most stable boron nitride structure with quantum simulations

Tyler O'Neal, Staff Editor BIG DATA

Researchers settle the debate on the relative stabilities of boron nitride’s structures using a state-of-the-art quantum simulation method

Boron nitride (BN) is a versatile material with applications in a variety of engineering and scientific fields. This is largely due to an interesting property of BN called “polymorphism,” characterized by the ability to crystallize into more than one type of structure. This generally occurs as a response to changes in temperature, pressure, or both. Furthermore, the different structures, called “polymorphs,” differ remarkably in their physical properties despite having the same chemical formula. As a result, polymorphs play an important role in material design, and a knowledge of how to selectively favor the formation of the desired polymorph is crucial in this regard. The structures and space groups of (a) zinc-blende boron nitride (cBN), (b) hexagonal boron nitride (hBN), (c) wurtzite boron nitride (wBN), and (d) rhombohedral boron nitride (rBN). Boron and nitrogen atoms are depicted in brown and blue, respectively. CREDIT Kousuke Nakano from JAIST.

However, BN polymorphs pose a particular problem. Despite conducting several experiments to assess the relative stabilities of BN polymorphs, a consensus has not emerged on this topic. While computational methods are often the go-to approach for these problems, BN polymorphs have posed serious challenges to standard computation techniques due to the weak “van der Waals (vdW) interactions” between their layers, which are not accounted for in these computations. Moreover, the four stable BN polymorphs, namely rhombohedral (rBN), hexagonal (hBN), wurtzite (wBN), and zinc-blende (cBN) manifest within a narrow energy range, making the capture of small energy differences together with vdW interactions even more challenging.

Fortunately, an international research team led by Assistant Professor Kousuke Nakano from the Japan Advanced Institute of Science and Technology (JAIST) has now provided evidence to settle the debate. In their study, they addressed the issue with a state-of-the-art first-principles calculations framework, namely fixed-node diffusion Monte Carlo (FNDMC) simulations. FNDMC represents a step in the popular quantum Monte Carlo simulations method, in which a parametrized many-body quantum “wavefunction” is first optimized to attain the ground state and then supplied to the FNDMC.

Additionally, the team also computed the Gibbs energy (the useful work obtainable from a system at constant pressure and temperature) of BN polymorphs for different temperatures and pressures using density functional theory (DFT) and phonon calculations. This paper was made available online on March 24, 2022, published in The Journal of Physical Chemistry C.

According to the FNDMC results, hBN was the most stable structure, followed by rBN, cBN, and wBN. These results were consistent at both 0 K and 300 K (room temperature). However, the DFT estimations yielded conflicting results for two different approximations. Dr. Nakano explains these contradictory findings: “Our results reveal that the estimation of relative stabilities is greatly influenced by the exchange correlational functional or the approximation used in the DFT calculation. As a result, a quantitative conclusion cannot be reached using DFT findings, and a more accurate approach, such as FNDMC, is required.”

Notably, the FNDMC results were in agreement with that generated by other refined computation methods, such as “coupled cluster,” suggesting that FNDMC is an effective tool for dealing with polymorphs, especially those governed by vdW forces. The team also showed that it can provide other important information, such as reliable reference energies, when experimental data is unavailable.

Dr. Nakano is excited about the prospects of the method in the area of materials science. “Our study demonstrates the ability of FNDMC to detect tiny energy changes involving vdW forces, which will stimulate the use of this method for other van der Waals materials,” he says. “Moreover, molecular simulations based on this accurate and reliable method could empower material designs, enabling the development of medicines and catalysts.”

Solving the relative stability conundrum is undoubtedly a huge step forward. But there is still work to be done and the pace is sure to go up!