Lifeboat Foundation

Science Daily reports that the astronomers found out that the mass of this lone white dwarf is equivalent to 56% of the sun’s weight. It aligns with previous theoretical predictions regarding the white dwarf’s mass, and it also sheds light on persisting theories regarding the evolution of these white dwarfs as a result of usual star evolution. The interesting observation grants further understanding of theories regarding white dwarf composition and structure.

According to the Space Academy, the astronomers made use of the renowned Hubble Space Telescope to gauge this lone white dwarf’s mass. The dwarf is known as LAWD 37.

Many of the proteins that play a crucial role in living cells adhere to a core principle of biology: their form, or shape, fits their function. But there is also a vast number of proteins and their parts that defy that dogma.

Why it matters: New findings are revealing how these flexible, disordered proteins work — and deciphering their role in human diseases and potential treatments.

How it works: Whether many medicines, immune cells, or the moment-to-moment inner workings of cells function depends on the shape of proteins they interact with or use.

Researchers are starting to unravel one of the biggest mysteries behind the AI language models that power text and image generation tools like DALL-E and ChatGPT.

For a while now, machine learning experts and scientists have noticed something strange about large language models (LLMs) like OpenAI’s GPT-3 and Google’s LaMDA : they are inexplicably good at carrying out tasks that they haven’t been specifically trained to perform. It’s a perplexing question, and just one example of how it can be difficult, if not impossible in most cases, to explain how an AI model arrives at its outputs in fine-grained detail.

In a forthcoming study posted to the arXiv preprint server, researchers at the Massachusetts Institute of Technology, Stanford University, and Google explore this “apparently mysterious” phenomenon, which is called “in-context learning.” Normally, to accomplish a new task, most machine learning models need to be retrained on new data, a process that can normally require researchers to input thousands of data points to get the output they desire—a tedious and time-consuming endeavor.

Blue straggler stars are the weird grandparents of the galaxy: They should be old, but they act young. Finding and studying these strange stars helps us understand the complicated life cycles of normal, more well-behaved stars.

All stars follow a particular path in life, known as the main sequence. The moment they begin fusing hydrogen in their cores, they maintain a strict relationship between their brightness and temperature. Different stars will have different combinations of brightness and temperature, but they all obey the same relationship. For example, smaller stars, like red dwarfs, will be relatively dim but also cool, with their surfaces turning a characteristic shade of red. Medium stars, like the sun, will be both hotter and brighter, turning white. The largest stars will be both incredibly bright and extremely hot, making them appear blue.

A book entitled “Dinner on Mars” describes how to turn the dry, cold Martian world into a place to grow food for future inhabitants.

Gaseous planets close to their star could see their hydrogen atmospheres evaporate, leaving behind helium that could be detected by the James Webb Space Telescope.

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A ring looping around the icy dwarf planet Quaoar is located much farther from its parent body than scientists thought was possible.

Dramatic advances in quantum computing, smartphones that only need to be charged once a month, trains that levitate and move at superfast speeds. Technological leaps like these could revolutionize society, but they remain largely out of reach as long as superconductivity—the flow of electricity without resistance or energy waste—isn’t fully understood.

One of the major limitations for real-world applications of this technology is that the materials that make superconducting possible typically need to be at extremely cold temperatures to reach that level of electrical efficiency. To get around this limit, researchers need to build a clear picture of what different superconducting materials look like at the atomic scale as they transition through different states of matter to become superconductors.

Scholars in a Brown University lab, working with an international team of scientists, have moved a small step closer to cracking this mystery for a recently discovered family of superconducting Kagome metals. In a new study, they used an innovative new strategy combining nuclear magnetic resonance imaging and a quantum modeling theory to describe the microscopic structure of this superconductor at 103 degrees Kelvin, which is equivalent to about 275 degrees below 0 degrees Fahrenheit.