Ninety-three years ago, a scientist trapped a mouse in a stream in Ethiopia. Of all the mice, rats, and gerbils in Africa, it stood out as the one most adapted for living in water, with water-resistant fur and long, broad feet. That specimen, housed at Chicago’s Field Museum, is the only one of its genus ever collected, and scientists think it may now be extinct. But in a new study in the Zoological Journal of the Linnean Society, researchers have verified this semi-aquatic mouse’s closest cousins, including two species new to science.
“These two groups of mice have been confused with one another for a century,” says Julian Kerbis Peterhans, one of the paper’s authors and a researcher at the Field Museum who’s studied these rodents for over 30 years. “They’ve been so elusive for so long, they’re some of the rarest animals in the world, so it’s exciting
Physicists are hatching a plan to give a popular but elusive dark-matter candidate a last chance to reveal itself. For decades, physicists have hypothesized that weakly interacting massive particles (WIMPs) are the strongest candidate for dark matter — the mysterious substance that makes up 85% of the Universe’s mass. But several experiments have failed to find evidence for WIMPs, meaning that, if they exist, their properties are unlike those originally predicted. Now, researchers are pushing to build a final generation of supersensitive detectors — or one ‘ultimate’ detector — that will leave the particles no place to hide.
“The WIMP hypothesis will face its real reckoning after these next-generation detectors run,” says Mariangela Lisanti, a physicist at Princeton University in New Jersey.
Physicists have long predicted that an invisible substance, which has mass but doesn’t interact with light, permeates the Universe. The gravitational effects of dark matter would explain why
Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.
DOE/SLAC National Accelerator Laboratory
Machine learning is ubiquitous in science and technology these days. It outperforms traditional computational methods in many areas, for instance by vastly speeding up tedious processes and handling huge batches of data. At the Department of Energy’s SLAC National Accelerator Laboratory, machine learning is already opening new avenues to advance the lab’s unique scientific facilities and research.
For example, SLAC scientists have already used machine learning techniques to operate accelerators more efficiently, to speed up the discovery of new materials, and to uncover distortions in space-time caused by astronomical objects up to 10 million times faster than traditional methods.
The term “machine learning” broadly refers to techniques that let computers “learn by example” by inferring their own conclusions from large sets of