National Science Foundation (NSF)

Newsweek’s Amanda Greenwood highlights Lumina, a simulation developed by a team of researchers from MIT and other institutions, that allows users “to follow the emergence of stars and galaxies across the first 2 billion years of cosmic history and compare the simulation's predictions with observations from modern telescopes.” Lumina “follows the evolution of dark matter, gas, stars and radiation across a volume of space about 1.6 billion light-years wide,” writes Greenwood. “Scientists can use the simulation to study both large-scale cosmic structure and the formation of individual galaxies.”  

The Lumina simulation, developed in part by MIT researchers, imagines how the first 2 billion years of the universe evolved to produce “galaxies and all their components,” writes Alfredo Carpineti for IFL Science. “Understanding the evolution of galaxies is key to understanding why we are here,” Carpineti writes. “The elements that made us came either directly from the Big Bang or were forged in stars long gone. The early universe is a very remote past, but what happens there is not just visible in the sky; it is in ourselves too.” 

A team of astronomers, including scientists from MIT, have developed Lumina, a complex computer simulation that “shows how the first galaxies, stars and other astral phenomena developed and evolved over the ensuing millennia,” reports Eric Lagatta for USA Today. “Lumina's simulations begin around 45 million years after the ‘Big Bang,’ when the universe is still ensconced in a cosmic dark age, as neutral hydrogen shrouded the universe, preventing light and other forms of radiation from traveling,” writes Lagatta. “The simulation covers a massive patch of the early universe, spanning about 1.6 billion light-years, as phenomena like dark matter, gas and stars form and grow.” 

Forbes reporter Marshall Shepherd highlights the Probabilistic Bias Correction tool, a new AI system developed by MIT researchers that could extend weather forecasting predictability beyond two weeks.  

Using satellite data, graduate student Caroline Needell and her colleagues found that the risk of runaway collapse in tall ice cliffs “depends on two main factors: how steeply the sea floor beneath the glacial ice deepens, and how quickly the ice flows seawards,” writes Marissa Grunes for New Scientist. “This idea has been suggested by idealized mathematical models, but had never been systematically measured in real glaciers,” notes Grunes. “The key question, says Needell, is whether thinning [from runaway collapse] can outcompete rapid ice loss, restabilizing the glacier. Her study suggests that it might.”  

MIT researchers have developed the Probabilistic Bias Correction (PBC) tool, which “uses machine learning to identify systematic bias in a model and make corrections” to improve weather forecasts, reports The Verge’s Justine Calma. “The researchers found that PBC improved the accuracy of a leading physics-based weather model by the European Centre for Medium-Range Weather Forecasts (ECMWF) for 98 percent of precipitation and 92 percent of temperature targets,” notes Calma. “The machine learning framework also doubled the accuracy of ECMWF’s new AI forecasting system in the study’s analysis.” 

Gizmodo’s Ellyn Lapointe spotlights the Probabilistic Bias Correction (PBC) tool developed by MIT researchers to “correct systemic errors in forecast models and improve the accuracy of their subseasonal outlooks.” “Extreme weather has the largest impact on our society and improved forecasts of these events can yield large societal benefits,” says Research Scientist Judah Cohen. “Interestingly, previous bias correction schemes actually made forecasts of extreme weather worse, and our model showed clear improvement of extreme weather forecasts.”  

MIT researchers have developed an AI system “that not only minimizes forecasting errors, but learns from its inevitable mistakes through a method called postprocessing,” writes reporter Andrew Paul for Popular Science. “Their AI postprocessing integration blew the existing forecasting models out of the water,” notes Paul. “Such massive improvements could soon help the farmers and energy providers who already rely on subseasonal forecasts, but the benefits may even save lives.” 

MIT researchers have found that the gravitational pull of a red dwarf star is “siphoning material” from a brown dwarf star 300 to 500 light-years from Earth, writes reporter Will Dunham for Reuters. “This is the first observed example of this phenomenon,” says graduate student Aaron Householder. “The two objects are so close together that the entire orbit would fit inside the sun.” 

Research Scientist Julian Klein and Prof. Frances Ross speak with speak with Physics reporter Akashni Raghubar Latchanna about an electron microscopy technique they developed to move atoms within a material, creating new possibilities for engineering electrical, optical, and quantum properties. “If we can control the electronic or optical functions of the materials, we might potentially make those devices more brain-like, more portable, and less power-hungry,” Ross said. 

MIT astronomers have discovered a “star slowly and steadily consuming material from another low-mass object,” writes Catherine Stoddard for Fox News. “At the rate which the star is feeding, it’ll take likely hundreds of thousands, or maybe even billions of years for it to completely consume its nearby neighbor,” notes Stoddard.  

MIT researchers have discovered “a red dwarf [star] slowly and steadily consuming its orbital object, a brown dwarf [star],” in a process that could continue for millions of years, writes reporter Mary Randolph for the Scientific American. The discovery was “a happy accident,” says graduate student Aaron Householder. “We’re excited to see if we can find a star eating a planet or maybe even two planets eating each other—different types of combinations of these objects.”  

MIT researchers have developed Ataraxos, an AI system capable of winning the challenging game Stratego. The system could one day help decision makers with real-world problems that involve hidden information, reports Jacek Krywk for Ars Technica. “Board games have fixed rules and clear winners, while real-world problems like negotiations, financial markets, or military conflicts usually don’t,” writes Krywk. “But the Ataraxos team argues the gap is smaller than it looks, since tackling any real problem starts with building a simplified model of it.” 

Cailin Plunkett speaks to the Scientific American’s Mary Randolph about her research discovering that 14% of black holes may be hierarchical mergers, made from the merger of two smaller black holes. “At minimum, we’re finding evidence that there are these highly spinning and weirdly tilted black holes in the dataset, and the most straightforward way to interpret that, the Occam’s razor answer, is that these black holes probably came from previous mergers,” says Plunkett.  

The Financial Times’ Anjana Ahuja highlights Prof. Sangeeta Bhatia and Senior Postdoctoral Associate Vardhman Kumar’s work developing injectable “mini livers,” tissue grafts that could “take on some of the life-saving functions of liver cells: helping with blood clotting, detoxifying the bloodstream and breaking down medicines. The injectable grafts, they suggest, could also be used as a bridging treatment while patients wait for a transplant, with booster injections able to dial up the dose,” writes Ahuja.