Category: Astronomy

ENG: An international research team led by scientists from Waseda University and Tohoku University has identified an unusual quasar in the early Universe, offering new clues about how supermassive black holes grew so quickly. A quasar is an extremely bright object powered by a black hole that pulls in large amounts of gas and dust. As this material spirals inward, it releases vast energy, allowing the system to be detected across billions of light-years and studied as a window into the young Universe.

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ENG: Two PhD students from Sydney have achieved a remarkable milestone in space science by using artificial intelligence to restore the James Webb Space Telescope’s sharp vision from Earth. Louis Desdoigts and Max Charles developed a software system called AMIGO (Aperture Masking Interferometry Generative Observations) that corrected image blurring in the telescope’s infrared camera, eliminating the need for a physical repair mission. Their work builds on the Australian-designed Aperture Masking Interferometer, created by Professor Peter Tuthill, which allows astronomers to capture ultra-high-resolution images of stars and exoplanets through interferometry.

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ENG: Recent research suggests that Earth and the Milky Way may be sitting inside a vast cosmic void nearly a billion light-years wide. Astronomers studying the echoes of the Big Bang, known as baryon acoustic oscillations (BAOs), have found evidence that our local region of space could be much emptier than the average Universe. This finding may help resolve the long-standing “Hubble Tension,” the puzzling discrepancy between measurements of the Universe’s expansion rate when comparing distant regions to our own cosmic neighborhood. If our galaxy lies near the center of a large void, the apparent faster expansion of the local Universe could simply be an illusion created by the lower density of matter around us.

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ENG: An international team of astronomers used artificial intelligence (AI) and a powerful computing method called high-throughput computing (HTC) to learn new details about the black hole at the center of our Milky Way, known as Sagittarius A*. They trained a special type of AI called a Bayesian neural network using millions of computer-generated simulations. These simulations were created with the help of the Center for High Throughput Computing (CHTC), which allowed the researchers to break the task into millions of smaller jobs and run them across thousands of computers. This approach helped the team analyze and compare data from the Event Horizon Telescope (EHT) in much greater detail than ever before.

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