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Space

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AI Reveals Unsuspected Math Underlying Search For Exoplanets

Artist’s concept of a sun-like star (left) and a rocky planet about 60% larger than Earth in orbit in the star’s habitable zone. Gravitational microlensing has the ability to detect such planetary systems and determine the masses and orbital distances, even though the planet itself is too dim to be seen. (Image credit: NASA Ames/JPL-Caltech/T. Pyle)   Artificial intelligence (AI) algorithms trained on real astronomical observations now outperform astronomers in sifting through massive amounts of data to find new exploding stars, identify new types of galaxies and detect the mergers of massive stars, accelerating the rate of new discovery in…
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Astronomers Find Signature Of Magnetar Outbursts In Nearby Galaxies

Apart from black holes, magnetars may be the most extreme stars in the universe. With a diameter less than the length of Manhattan, they pack more mass than that of our sun, wield the largest magnetic field of any known object — more than 10 trillion times stronger than a refrigerator magnet — and spin on their axes every few seconds. A type of neutron star — the remnant of a supernova explosion — magnetars are so highly magnetized that even modest disturbances in the magnetic field can cause bursts of X-rays that last sporadically for weeks or months. These…
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Space Has Arrived: Space Exploration Initiative At Ars Electronica 2020

This year, we joined Ars Electronica In Kepler’s garden online program, a collective effort to reconnect. We hosted three interactive workshops, spanning a diverse range of SEI’s current research thrusts: Wellbeing in Extreme Environments, led by Maggie Coblentz; Interplanetary Time, Communication and Longevity, led by Che-wei Wang and Sands Fish; and A New Moon Landing, led by Mehak Sarang. In addition we featured an exhibition on Sojourner2020, the first international art open call that took a trip to the International Space Station this March, led by Xin Liu. Our digital spaceship is also live, and houses our diverse portfolio of projects. We…
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Machine Learning Makes Building Rocket Engines Easier

Methods from scientific machine learning could address the challenges of testing the stability of rocket engines, researchers report. Time, cost, and safety prohibit testing the stability of a test rocket using a physical build “trial and error” approach. But even computational simulations are extremely time consuming. A single analysis of an entire SpaceX Merlin rocket engine, for example, could take weeks, even months, for a supercomputer to provide satisfactory predictions. Scientific machine learning is a relatively new field that blends scientific computing with machine learning. Through a combination of physics modeling and data-driven learning, it becomes possible to create reduced-order models—simulations…
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China’s Quantum Satellite Enables First Totally Secure Long-Range Messages

In the middle of the night, invisible to anyone but special telescopes in two Chinese observatories, satellite Micius sends particles of light to Earth to establish the world’s most secure communication link. Named after the ancient Chinese philosopher also known as Mozi, Micius is the world’s first quantum communications satellite and has, for several years, been at the forefront of quantum encryption. Scientists have now reported using this technology to reach a major milestone: long-range secure communication you could trust even without trusting the satellite it runs through. Launched in 2016, Micius has already produced a number of breakthroughs under…
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Scientific Machine Learning Paves Way for Rapid Rocket Engine Design

“It’s not rocket science” may be a tired cliché, but that doesn’t mean designing rockets is any less complicated. Time, cost and safety prohibit testing the stability of a test rocket using a physical build “trial and error” approach. But even computational simulations are extremely time consuming. A single analysis of an entire SpaceX Merlin rocket engine, for example, could take weeks, even months, for a supercomputer to provide satisfactory predictions. One group of researchers at The University of Texas at Austin is developing new “scientific machine learning” methods to address this challenge. Scientific machine learning is a relatively new…
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Machine Learning Algorithms Hunt For Dark Matter In Space Maps

Understanding how our universe came to be what it is today and what its final destiny will be is one of the biggest challenges in science. The awe-inspiring display of countless stars on a clear night gives us some idea of the magnitude of the problem, and yet that is only part of the story. The deeper riddle lies in what we cannot see, at least not directly: dark matter and dark energy. With dark matter pulling the universe together and dark energy causing it to expand faster, cosmologists need to know exactly how much of those two is out…
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