How Stars Move At The Center Of The Galaxy: Figures Of 8 And Peanut Shells

Credit: ESO/NASA/JPL-Caltech/M. Kornmesser/R. Hurt'
Two months ago astronomers created a new 3D map of stars at the centre of our Galaxy (the Milky Way), showing more clearly than ever the bulge at its core. Previous explanations suggested that the stars that form the bulge are in banana-like orbits, but a paper published this week in Monthly Notices of the Royal Astronomical Society suggests that the stars probably move in peanut-shell or figure of eight-shaped orbits instead. An artist’s impression showing how the Milky Way galaxy would look seen from almost edge on and from a very different perspective than we get from the Earth. The central bulge shows up as a peanut shaped glowing ball of stars and the spiral arms and their associated dust clouds form a narrow band. The difference is important; astronomers develop theories of star motions to not only understand how the stars in our galaxy are moving today but also how our galaxy formed and evolves. The Milky Way is shaped like a spiral, with a region of stars at the centre known as the "bar," because of its shape. In the middle of this region, there is a "bulge" that expands out vertically. In the new paper Alice Quillen, professor of astronomy at the University of Rochester, and her collaborators created a mathematical model of what might be happening at the centre of the Milky Way. Unlike the Solar System where most of the gravitational pull comes from the Sun and is simple to model, it is much harder to describe the gravitational field near the centre of the Galaxy, where millions of stars, vast clouds of dust, and even dark matter swirl about. In this case, Quillen and her colleagues considered the forces acting on the stars in or near the bulge. As the stars go round in their orbits, they also move above or below the plane of the bar. When stars cross the plane they get a little push, like a child on a swing. At the resonance point, which is a point a certain distance from the center of the bar, the timing of the pushes on the stars is such that this effect is strong enough to make the stars at this point move up higher above the plane. It is like when the child on the swing has been pushed a little everytime he comes round and eventually he is swinging higher. These stars that are pushed out form the edge of the bulge. The resonance at this point means that stars undergo two vertical oscillations for every orbital period. But what is the most likely shape of the orbits in between? The researchers showed through computer simulations that peanut-shell shaped orbits are consistent with the effect of this resonance and could give rise to the observed shape of the bulge, which is also like a peanut-shell. Next month the European Space Agency will launch the Gaia spacecraft, which is designed to create a 3D map of the stars in the Milky Way and their motions. This 3D map will help astronomers better understand the composition, formation and evolution of our Galaxy. "It is hard to look back into the past of our galaxy and know what was there, but simulations can give us clues," explained Quillen. "Using my model I saw that, over time, the resonance with the bar, which is what leads to these peculiarly shaped orbits, moves outwards. This may be what happened in our galaxy." "Gaia will generate huge amounts of data – on billions of stars," said Quillen. This data will allow Quillen and her colleagues to finesse their model further. "This can lead to a better understanding of how the Milky Way might have evolved into the shape it has today." Quillen explained that there are different models as to how the galactic bulge was formed. Astronomers are interested in finding out how much the bar has slowed down over time and whether the bulge "puffed up all at once or slowly." Understanding the distributions of speeds and directions of motion (velocities) of the stars inthe bar and the bulge might help determine this evolution. "One of the predictions of my model is that there is a sharp difference in the velocity distributions inside and outside the resonance," Quillen said. "Inside – closer to the galactic centre – the disk should be puffed up and the stars there would have higher vertical velocities. Gaia will measure the motions of the stars and allow us to look for variations in velocity distributions such as these." Two movies of N-body simulations, one showing a bar that buckles (top), the other without buckling. These movies show face-on and edge-on views of barred galaxies. Both movies show that the peanut shape becomes more extended as the bar slows down.
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To be able to generate a model for the orbits of stars in the bulge, Quillen needed to factor in different variables. She first needed to understand what happens at the region of the resonance, which depends on the speed of the rotating bar and the mass density of the bar. "Before I could model the orbits, I needed the answer to what I thought was a simple question: what is the distribution of material in the inner galaxy?" Quillen said. "But this wasn't something I could just look up. Luckily my collaborator Sanjib Sharma was able to help out." Sharma worked out how the speed of circular orbits changed with distance from the galactic centre (called the rotation curve). Using this information, Quillen could compute a mass density at the location of the resonance, which she needed for her model. Quillen was also able to combine the new orbit models with the speed of the bar (which is rotating) to get a more refined estimate of the mass density 3000 light years from the Galaxy centre (about one eighth of the distance from the centre of the Galaxy to Earth), which is where the edge of the bulge is. And there is not long now to wait now for Gaia to start collecting data. Gaia's launch time is set for December 19, and will be streamed live on the ESA Portal. Quillen's co-authors in this paper are Sanjib Sharma, Sydney Institute for Astronomy, Australia; Ivan Minchev, Astronomy Institute of Potsdam, Germany; Yu-Jing Qin, Shanghai Astronomical Observatory, China; and Paola Di Matteo, Paris-Meudon Observatory, France. Contacts and sources: Leonor Sierra, University of RochesterSource: Article
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Genetic difference between tomato and potato only 8%


Fresh Plaza : Recent research has demonstrated that tomatoes and potatoes are 92% similar to each other in terms of genetics. The scientists behind the studies also discovered that tomato is closely related, in genetic terms, to strawberries, apples, melons and other fleshy fruits. The decoded genome of the tomato is an important step towards improving yield, nutrition, disease resistance, taste and colour of tomato and other crops, scientis-ts said. "The genetic divergence between tomato and potato is only 8 percent. There are only about 500 genes specific to tomato," explained Dr Akhilesh Kumar Tyagi, Director, National Institute of Plant Genome Research, one of the three Indian members of the international tomato consortium. Though potato is a tuber and tomato a fruit, they belong to the same family - Solanaceae. "The similarities between the two relate to genes that control important traits like disease resistance and metabolism," explained Dr Tyagi. Comparisons between genomes of wild and cultivated varieties showed the difference was less than one per cent, though many changes have occurred since domestication of the wild tomato and the intensive breeding that followed. In fact, wild tomato is very small, almost the size of a pea. Consortium researchers report that tomatoes possess close to 35,000 genes arranged on 12 chromosomes. "For any characteristic of the tomato, whether it's taste, natural pest resistance or nutritional content, we've captured virtually all those genes," said James Giovannoni of Boyce Thompson Institute for Plant Research, who led the 14-country consortium that started the project in 2003. Source: Fresh PlazaImage: flickr.com
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Are You Ready for Benevolent Artificial Intelligence

Credit: © IMAGO / Jochen Tack                                                                     Autonomous bus, in Monheim, Rhine

Picture yourself driving on a narrow road in the near future when suddenly another car emerges from a bend ahead. It is a self-driving car with no passengers inside. Will you push forth and assert your right of way, or give way to let it pass? At present, most of us behave kindly in such situations involving other humans. Will we show that same kindness towards autonomous vehicles?

Using methods from behavioural game theory, an international team of researchers at LMU Munich and the University of London have conducted large-scale online studies to see whether people would behave as cooperatively with artificial intelligence (AI) systems as they do with fellow humans.

Cooperation holds a society together. It often requires us to compromise with others and to accept the risk that they let us down. Traffic is a good example. We lose a bit of time when we let other people pass in front of us and are outraged when others fail to reciprocate our kindness. Will we do the same with machines?

The study which is published in the journal iScience found that, upon first encounter, people have the same level of trust toward AI as for human: most expect to meet someone who is ready to cooperate.The difference comes afterwards. People are much less ready to reciprocate with AI, and instead exploit its benevolence to their own benefit. Going back to the traffic example, a human driver would give way to another human but not to a self-driving car.The study identifies this unwillingness to compromise with machines as a new challenge to the future of human-AI interactions.

Credit: Pixabay

“We put people in the shoes of someone who interacts with an artificial agent for the first time, as it could happen on the road,” explains Jurgis Karpus, Ph.D., a behavioural game theorist and a philosopher at LMU Munich and the first author of the study. “We modelled different types of social encounters and found a consistent pattern. People expected artificial agents to be as cooperative as fellow humans. However, they did not return their benevolence as much and exploited the AI more than humans.”

With perspectives from game theory, cognitive science, and philosophy, the researchers found that ‘algorithm exploitation’ is a robust phenomenon. They replicated their findings across nine experiments with nearly 2,000 human participants. Each experiment examines different kinds of social interactions and allows the human to decide whether to compromise and cooperate or act selfishly. Expectations of the other players were also measured. In a well-known game, the Prisoner’s Dilemma, people must trust that the other characters will not let them down. They embraced risk with humans and AI alike, but betrayed the trust of the AI much more often, to gain more money.

“Cooperation is sustained by a mutual bet: I trust you will be kind to me, and you trust I will be kind to you. The biggest worry in our field is that people will not trust machines. But we show that they do!” notes Dr. Bahador Bahrami, a social neuroscientist at the LMU, and one of the senior researchers in the study. “They are fine with letting the machine down, though, and that is the big difference. People even do not report much guilt when they do,” he adds.

Biased and unethical AI has made many headline — from the 2020 exams fiasco in the United Kingdom to justice systems — but this new research brings up a novel caution. The industry and legislators strive to ensure that artificial intelligence is benevolent. But benevolence may backfire. If people think that AI is programmed to be benevolent towards them, they will be less tempted to cooperate. Some of the accidents involving self-driving cars may already show real-life examples: drivers recognize an autonomous vehicle on the road, and expect it to give way. The self-driving vehicle meanwhile expects for normal compromises between drivers to hold.“

Algorithm exploitation has further consequences down the line. "If humans are reluctant to let a polite self-driving car join from a side road, should the self-driving car be less polite and more aggressive in order to be useful?” asks Jurgis Karpus.

“Benevolent and trustworthy AI is a buzzword that everyone is excited about. But fixing the AI is not the whole story. If we realize that the robot in front of us will be cooperative no matter what, we will use it to our selfish interest,” says Professor Ophelia Deroy, a philosopher and senior author on the study, who also works with Norway’s Peace Research Institute Oslo on the ethical implications of integrating autonomous robot soldiers along with human soldiers.

“Compromises are the oil that make society work. For each of us, it looks only like a small act of self-interest. For society as a whole, it could have much bigger repercussions. If no one lets autonomous cars join the traffic, they will create their own traffic jams on the side, and not make transport easier”.

Contacts and sources:

Ludwig-Maximilians-Universität München

Publication: Algorithm exploitation: humans are keen to exploit benevolent AI.

Jurgis Karpus, Adrian Krüger, Julia Tovar Verba, Bahador Bahrami, Ophelia Deroy. iScience, 2021; 102679 DOI: 10.1016/j.isci.2021.102679 Ideas, Source: https://www.ineffableisland.com/
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