Tyler O'Neal, Staff Editor PROCESSORS

UConn's new study using simulations of thermonuclear deflagrations sheds light on conditions that trigger supernovae explosions

Research offers a critical understanding of a process both in stars and in chemical systems on Earth.

Understanding the thermonuclear explosion of Type Ia supernovae -- powerful and luminous stellar explosions -- is only possible through theoretical models, which previously were not able to account for the mechanism that detonated the explosion.

One of the key pieces of this explosion, present virtually in all models, is the formation of a supersonic reaction wave called detonation, which can travel faster than the speed of sound and is capable of burning up all of the material of a star before it gets dispersed into the vacuum of space.

But, the physics of the mechanisms that create a detonation in a star has been elusive.

Now, a team of researchers from the University of Connecticut, Texas A&M University, University of Central Florida, Naval Research Laboratory, and Air Force Research Laboratory has developed a theory that sheds light on the enigmatic process of detonation formation at the heart of these remarkable astronomical events.

The research, published Nov. 1 in Science, offers a critical understanding of this physical process both in stars and also in chemical systems on Earth. It was led by Alexei Poludnenko, UConn School of Engineering and Texas A&M University; in collaboration with Jessica Chambers and Kareem Ahmed, the University of Central Florida; Vadim Gamezo, the Naval Research Laboratory; and Brian Taylor, the Air Force Research Laboratory.

For the first time, researchers were able to demonstrate the process of detonation formation from a slow subsonic flame using both experiments and numerical simulations carried out on some of the largest supercomputers in the nation. They also successfully applied the results to predict the conditions of detonation formation in one of the classical theoretical scenarios of Type Ia supernova explosion.

Type Ia supernovae explosions happen when carbon and oxygen packed to a density of around 1,000 tons per cubic centimeter in the stellar core burn in quick, thermonuclear reactions. The resulting explosion disrupts a star in a matter of seconds and ejects most of its mass while emitting an amount of energy equal to the energy emitted by the star over its entire lifetime.

Typically, in order to form a detonation, burning must occur in a confined setting with walls, obstacles, or boundaries, which can confine pressure waves being released by burning.

As pressure rises, shock waves form, which can grow in strength to the point when they can compress the reacting mixture igniting it and producing a self-sustaining supersonic front. Stars do not have walls or obstacles, which makes the formation of a detonation enigmatic.

In this study, the team developed a unified theory of turbulence-induced deflagration-to-detonation that describes the mechanism and conditions for initiating detonation both in unconfined chemical and thermonuclear explosions.

According to the theory, if one takes the reactive mixture, which burns and releases energy, and stirs it up to create intense turbulence, a catastrophic instability can result and would rapidly increase pressure in the system producing strong shocks and igniting a detonation. Remarkably this theory predicts the conditions for detonation formation in Type Ia supernovae.

Researchers were able to gain insight into the fundamental aspects of the physical processes that control supernovae explosions because thermonuclear combustion waves are similar to chemical combustion waves on Earth in that they are controlled by the same physical mechanisms.

Because of the similarities, the findings may  be applied to various terrestrial combustion systems in which detonations can form, such as the context of industrial accidents involving gaseous explosions, as well as novel propulsion and energy conversion applications, such as detonation-based engines.

Dartmouth engineers develop new way to know liars' intent

Tyler O'Neal, Staff Editor PROCESSORS

The algorithm could bolster readers' interpretation of 'fake news'

Dartmouth engineering researchers have developed a new approach for detecting a speaker's intent to mislead. The approach's framework, which could be developed to extract opinion from "fake news," among other uses, was recently published as part of a paper in the Journal of Experimental & Theoretical Artificial Intelligence.

Although previous studies have examined deception, this is possibly the first study to look at a speaker's intent. The researchers posit that while a true story can be manipulated into various deceiving forms, the intent, rather than the content of the communication, determines whether the communication is deceptive or not. For example, the speaker could be misinformed or make a wrong assumption, meaning the speaker made an unintentional error but did not attempt to deceive.

"Deceptive intent to mislead listeners on purpose poses a much larger threat than unintentional mistakes," said Eugene Santos Jr., co-author, and professor of engineering at Thayer School of Engineering at Dartmouth. "To the best of our knowledge, our algorithm is the only method that detects deception and at the same time discriminates malicious acts from benign acts." 

Eugene Santos Jr. is a professor of engineering at Thayer School of Engineering at Dartmouth.
Eugene Santos Jr. is a professor of engineering at Thayer School of Engineering at Dartmouth.

{module In-article} The researchers developed a unique approach and the resulting algorithm that can tell deception apart from all benign communications by retrieving the universal features of deceptive reasoning. However, the framework is currently limited by the amount of data needed to measure a speaker's deviation from their past arguments; the study used data from a 2009 survey of 100 participants on their opinions on controversial topics, as well as a 2011 dataset of 800 real and 400 fictitious reviews of the same 20 hotels.

Santos believes the framework could be further developed to help readers distinguish and closely examine the intent of "fake news," allowing the reader to determine if a reasonable, logical argument is used or if opinion plays a strong role. In further studies, Santos hopes to examine the ripple effect of misinformation, including its impacts.

In the study, the researchers use the popular 2001 film Ocean's Eleven to illustrate how the framework can be used to examine a deceiver's arguments, which in reality may go against his true beliefs, resulting in a falsified final expectation. For example, in the movie, a group of thieves breaks into a bank vault while simultaneously revealing to the owner that he is being robbed to negotiate. The thieves supply the owner with false information, namely that they will only take half the money if the owner doesn't call the police. However, the thieves expect the owner to call the police, which he does, so the thieves then disguise themselves as police to steal the entirety of the vault contents.

Because Ocean's Eleven is a scripted film, viewers can be sure of the thieves' intent - steal all of the money - and how it conflicts with what they tell the owner - that they will only take half. This illustrates how the thieves were able to deceive the owner and anticipate his actions because the thieves and owner had different information and therefore perceived the scene differently.

"People expect things to work in a certain way," said Santos, "just like the thieves knew that the owner would call the police when he found out he was being robbed. So, in this scenario, the thieves used that knowledge to convince the owner to come to a certain conclusion and follow the standard path of expectations. They forced their deception intent so the owner would reach the conclusions the thieves desired."

In popular culture, verbal and non-verbal behaviors such as facial expressions are often used to determine if someone is lying, but the co-authors note that those cues are not always reliable.

"We have found that models based on reasoning intent are more reliable than verbal changes and personal differences, and thus are better at distinguishing intentional lies from other types of information distortion," said co-author Deqing Li, who worked on the paper as part of her Ph.D. thesis at Thayer.

OSU scientists may have discovered whole new class of black holes

Tyler O'Neal, Staff Editor PROCESSORS

These are smaller than researchers believed possible

Black holes are an important part of how astrophysicists make sense of the universe - so important that scientists have been trying to build a census of all the black holes in the Milky Way galaxy.

But new research shows that their search might have been missing an entire class of black holes that they didn't know existed.

In a study published today in the journal Science, astronomers offer a new way to search for black holes and show that there may be a class of black holes smaller than the smallest known black holes in the universe.

"We're showing this hint that there is another population out there that we have yet to probe in the search for black holes," said Todd Thompson, a professor of astronomy at The Ohio State University and lead author of the study.

"People are trying to understand supernova explosions, how supermassive black stars explode, how the elements were formed in supermassive stars. So if we could reveal a new population of black holes, it would tell us more about which stars explode, which don't, which form black holes, which form neutron stars. It opens up a new area of study."

Imagine a census of a city that only counted people 5'9" and taller - and imagine that the census takers didn't even know that people shorter than 5'9" existed. Data from that census would be incomplete, providing an inaccurate picture of the population. That is essentially what has been happening in the search for black holes, Thompson said.

Astronomers have long been searching for black holes, which have gravitational pulls so fierce that nothing - not matter, not radiation - can escape. Black holes form when some stars die, shrink into themselves and explode. Astronomers have also been looking for neutron stars - small, dense stars that form when some stars die and collapse.

Both could hold interesting information about the elements on Earth and about how stars live and die. But to uncover that information, astronomers first have to figure out where the black holes are. And to figure out where the black holes are, they need to know what they are looking for.

One clue: Black holes often exist in something called a binary system. This simply means that two stars are close enough to one another to be locked together by gravity in a mutual orbit around one another. When one of those stars dies, the other can remain, still orbiting the space where the dead star - now a black hole or neutron star - once lived, and where a black hole or neutron star has formed.

For years, the black holes scientists knew about were all between approximately five and 15 times the mass of the sun. The known neutron stars are generally no bigger than about 2.1 times the mass of the sun - if they were above 2.5 times the sun's mass, they would collapse to a black hole

But in the summer of 2017, a survey called LIGO - the Laser Interferometer Gravitational-Wave Observatory - saw two black holes merging in a galaxy about 1.8 million light-years away. One of those black holes was about 31 times the mass of the sun; the other about 25 times the mass of the sun.

"Immediately, everyone was like 'wow,' because it was such a spectacular thing," Thompson said. "Not only because it proved that LIGO worked, but because the masses were huge. Black holes that size are a big deal - we hadn't seen them before."

Thompson and other astrophysicists had long suspected that black holes might come in sizes outside the known range, and LIGO's discovery proved that black holes could be larger. But there remained a window of size between the biggest neutron stars and the smallest black holes.

Thompson decided to see if he could solve that mystery.

He and other scientists began combing through data from APOGEE, the Apache Point Observatory Galactic Evolution Experiment, which collected light spectra from around 100,000 stars across the Milky Way. The spectra, Thompson realized, could show whether a star might be orbiting around another object: Changes in spectra - a shift toward bluer wavelengths, for example, followed by a shift to redder wavelengths - could show that a star was orbiting an unseen companion.

Thompson began combing through the data, looking for stars that showed that change, indicating that they might be orbiting a black hole.

Then, he narrowed the APOGEE data to 200 stars that might be most interesting. He gave the data to a graduate research associate at Ohio State, Tharindu Jayasinghe, who compiled thousands of images of each potential binary system from ASAS-SN, the All-Sky Automated Survey for Supernovae. (ASAS-SN has found some 1,000 supernovae, and is run out of Ohio State.)

Their data-crunching found a giant red star that appeared to be orbiting something, but that something, based on their calculations, was likely much smaller than the known black holes in the Milky Way, but way bigger than most known neutron stars.

After more calculations and additional data from the Tillinghast Reflector Echelle Spectrograph and the Gaia satellite, they realized they had found a low-mass black hole, likely about 3.3 times the mass of the sun.

"What we've done here is come up with a new way to search for black holes, but we've also potentially identified one of the first of a new class of low-mass black holes that astronomers hadn't previously known about," Thompson said. "The masses of things tell us about their formation and evolution, and they tell us about their nature."