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From UChicago News, Oct. 15, 2019: Astronomers at the University of Chicago, MIT and elsewhere have used a massive cluster of galaxies as an X-ray magnifying glass to peer back in time, to nearly 9.4 billion years ago. In the process, they spotted a tiny dwarf galaxy in its very first, high-energy stages of star formation. Fermilab and University of Chicago scientist Brad Benson is a co-author of the study.
In the last few decades, Argentina and Chile have proven themselves prime spots for astronomical observation — a status that has been a boon in many ways for both countries.
Early Tuesday morning, three physicists—James Peebles, Michel Mayor and Didier Queloz—were rewarded for decades seminal contributions to advancing science with a phone call from Stockholm. This year’s Nobel Prize in Physics was awarded “for contributions to our understanding of the evolution of the universe and Earth’s place in the cosmos.”
From APS’s Physics, Oct. 3, 2019: Fermilab scientist Brian Nord imagines a future where machines test hypotheses on their own — and considers the challenges ahead as scientists embrace artificial intelligence techniques. Nord has begun applying AI to problems in astronomy, such as identifying unusual astronomical objects known as gravitational lenses. He spoke to Physics about his recent projects and how he thinks AI will change the way researchers do science.
From UChicago News, Oct. 1, 2019: AI technology is increasingly used to open up new horizons for scientists and researchers. At the University of Chicago, researchers are using it look for supernovae, find new drugs and develop a deeper understanding of Earth’s climate. University of Chicago and Fermilab scientist Brian Nord is partnering exploring a “self-driving telescope:” a framework that could optimize when and where to point telescopes to gather the most interesting data.
When LIGO and Virgo detected the echoes that likely came from a collision between a black hole and a neutron star, dozens of physicists began a hunt for the signal’s electromagnetic counterpart.
When he was growing up, Jonathan LeyVa thought he’d follow his passion for race cars and pick a profession in automotive engineering. Instead he’s working on what will become one of the world’s most sensitive searches for dark matter, the invisible substance that accounts for more than 85% of the mass of the universe.
From Inside HPC, Sept. 15, 2019: Argonne and the National Center for Supercomputing Applications use deep learning to analyze Dark Energy Survey data.
A new study shows that light dark matter is 1000 times less likely to bump into regular matter than previous analyses allowed.
Few numbers have gotten under astronomers’ skin like the Hubble constant. In fact, experts have debated the value of this single parameter for 90 years, and if astronomers can measure its value with great precision, they’ll be one step closer to solving some of the other grand astronomical mysteries of our age. There’s just one problem: The measurements they’ve taken don’t agree. The discrepancy makes scientists question whether something is amiss in our understanding of the universe.