Lifeboat Foundation

Black holes are regions in space where the gravitational pull is so strong that nothing can escape them, not even light. These fascinating regions have been the focus of countless studies, yet some of the physics underlying their formation is not yet fully understood.

Black holes are formed in what is known as gravitational collapse. This is essentially the contraction of a cosmological object, prompted by its own gravity drawing matter inward (i.e., toward the object’s center of gravity).

Whether or not such a collapsing object forms a black hole depends on the specific properties of the object. In some cases, an object may be very close to the threshold, having a hard time deciding whether or not to form a black hole. This type of collapse results in so-called critical phenomena.

Six archival Chandra observations are matched with eight sets of radio data and studied in the context of the outflow method to measure and study the spin properties of |$\rm {Sgr ~A^{*}}$|⁠. Three radio and X-ray data sets obtained simultaneously, or partially simultaneously, are identified as preferred for the purpose of measuring the spin properties of |$\rm {Sgr ~A^{*}}$|⁠. Similar results are obtained with other data sets. Results obtained with the preferred data sets are combined and indicate weighted mean values of the spin function of |$F = 0.62 \pm 0.10$| and dimensionless spin angular momentum of |$a_* = 0.90 \pm 0.06$|⁠

A new study using data from the CALET instrument on the ISS has found evidence for young and nearby sources of cosmic ray electrons from supernova remnants.

NASA

In a new study using data from the CALorimetric Electron Telescope (CALET) instrument on the ISS, the researchers have found evidence for young and nearby sources of cosmic ray electrons, which are a special kind of cosmic ray that carry a negative charge. These sources will likely be the remnants of exploded stars, or supernovae, in our galactic neighborhood.

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A recent study published in Astrophysical Journal Letters discusses how new data from NASA’s James Webb Space Telescope (JWST) has identified the second-and fourth-farthest and oldest galaxies in the universe, which are located approximately 33 billion light years from Earth and part of Abell 2744, also known as Pandora’s Cluster. The reason the galaxies are estimated to be 33 billion light years from Earth is due to the expansion of the universe, but astronomers hypothesize the two were first formed approximately 330 million years after the Big Bang, which is incredibly young in cosmic terms.

The two galaxies are named UNCOVER z-12 and UNCOVER z-13 since they were discovered by the JWST UNCOVER (Ultradeep NIRSpec and NIRCam ObserVations before the Epoch of Reionization) team. This study was conducted by an international team of more than two dozen researchers, who refer to the two galaxies as appearing like a peanut and fluffy ball, and this study holds the potential to help scientists better understand the formation and evolution of the first galaxies after the Big Bang.

“Very little is known about the early universe, and the only way to learn about that time and to test our theories of early galaxy formation and growth is with these very distant galaxies,” said Dr. Bingjie Wang, who is a postdoctoral scholar in the Penn State Eberly College of Science and lead author of the study. “Prior to our analysis, we knew of only three galaxies confirmed at around this extreme distance. Studying these new galaxies and their properties has revealed the diversity of galaxies in the early universe and how much there is to be learned from them.”

A massive burst of gamma rays produced by the explosion of a star almost two billion light-years away was so powerful that it changed Earth’s atmosphere, according to scientists.

The brightest gamma-ray burst ever seen and detected impacted Earth’s atmosphere. It came from a supernova and may reveal why Earth has had mass extinctions in its past.

An international team of astrophysicists including Princeton’s Andy Goulding has discovered the most distant supermassive black hole ever found, using two NASA space telescopes: the Chandra X-ray Observatory (Chandra) and the James Webb Space Telescope (JWST).

The black hole, which is an estimated 10 to 100 million times more massive than our sun, is 13.2 billion light-years away in the galaxy UHZ-1, which means the telescopes are peering back in time to when the universe was “extremely young,” Goulding said — only about 450 million years old.

“This is one of the most dramatic discoveries to come out of the James Webb Space Telescope” and the discovery of the most distant growing supermassive black hole known, said Michael Strauss, professor and chair of astrophysical sciences at Princeton, who discussed the findings with the researchers but was not part of the research team. “Indeed, it completely smashes the old record.”

European astronomers released the first images from the new Euclid space telescope last week.

The European Space Agency (ESA) and the U.S. space agency, NASA, designed Euclid to study dark matter and dark energy. Scientists think those hidden forms of matter and energy make up 95 percent of the universe.

ESA is leading the six-year mission with NASA as a partner. ESA said the images were the most detailed of their kind. They show the telescope’s ability to observe billions of galaxies up to 10 billion light years away.

At the heart of a distant galaxy, scientists saw a fountain of material moving away from the central supermassive black hole — and back.

A new way to simulate supernovae may help shed light on our cosmic origins. Supernovae, exploding stars, play a critical role in the formation and evolution of galaxies. However, key aspects of them are notoriously difficult to simulate accurately in reasonably short amounts of time. For the first time, a team of researchers, including those from The University of Tokyo, apply deep learning to the problem of supernova simulation. Their approach can speed up the simulation of supernovae, and therefore of galaxy formation and evolution as well. These simulations include the evolution of the chemistry which led to life.

When you hear about deep learning, you might think of the latest app that sprung up this week to do something clever with images or generate humanlike text. Deep learning might be responsible for some behind-the-scenes aspects of such things, but it’s also used extensively in different fields of research. Recently, a team at a tech event called a hackathon applied deep learning to weather forecasting. It proved quite effective, and this got doctoral student Keiya Hirashima from the University of Tokyo’s Department of Astronomy thinking.

“Weather is a very complex phenomenon but ultimately it boils down to fluid dynamics calculations,” said Hirashima. “So, I wondered if we could modify deep learning models used for weather forecasting and apply them to another fluid system, but one that exists on a vastly larger scale and which we lack direct access to: my field of research, supernova explosions.”