Here are some of the Space Science focused articles we at HubBucket Inc (“HubBucket”) are reading this month (September 2024), that we think you may be interested in reading as well. The links to the full article on the Simons Foundation (website) is provided with each of the article introductions. HubBucket Inc (“HubBucket”) Official Website: http://hubbucket.xyz
ARTICLE ONE (1) | New Detectable Gravitational Wave Source From Collapsing Stars Predicted From Simulations (September 2024)
The Ripples in Space-Time caused by the Death of Nassive Spinning Stars could be within the limits of detection of projects like LIGO and Virgo, new simulations by Flatiron Institute Astrophysicists suggest.
The death of a massive, rapidly spinning star can shake the universe. And the resulting ripples — known as gravitational waves — could be felt by instruments on Earth, according to new research published August 22 in The Astrophysical Journal Letters. These new sources of gravitational waves just await discovery, the scientists behind the research predict.
The gravitational waves emerge following the violent deaths of rapidly rotating stars 15 to 20 times the mass of the sun. Upon running out of fuel, these stars implode, then explode, in an event known as a collapsar. This leaves behind a black hole surrounded by a large disk of leftover material that quickly whirls into the black hole’s maw. The spiraling of material — which lasts just minutes — is so great that it distorts the space around it, creating gravitational waves that travel across the universe.
Using cutting-edge simulations, the scientists determined that these gravitational waves could be detectable with instruments like the Laser Interferometer Gravitational-Wave Observatory (LIGO), which made the first direct observations of gravitational waves from merging black holes in 2015. If spotted, the collapsar-driven waves would help scientists understand the mysterious inner workings of collapsars and black holes.
“Currently, the only gravitational wave sources that we have detected come from a merger of two compact objects — neutron stars or black holes,” says study lead Ore Gottlieb, a research fellow at the Flatiron Institute’s Center for Computational Astrophysics (CCA) in New York City. “One of the most interesting questions in the field is: What are the potential non-merger sources that could produce gravitational waves that we can detect with current facilities? One promising answer is now collapsars.”
Gottlieb, along with CCA visiting scholar and Columbia professor Yuri Levin and Tel Aviv University professor Amir Levinson, simulated the conditions — including magnetic fields and cooling rates — found in the aftermath of a massive rotating star’s collapse. The simulations showed that collapsars can produce gravitational waves powerful enough to be visible from about 50 million light-years away. That distance is less than one-tenth the detectable range of the more powerful gravitational waves from mergers of black holes or neutron stars, though it’s still stronger than any non-merger event yet simulated.
The new findings come as a surprise, Gottlieb says. Scientists thought the chaotic collapse would create a jumble of waves that would be hard to pick out amid the universe’s background noise. Think of an orchestra warming up. When each musician plays their own notes, it can be hard to distinguish the melody coming from a single flute or tuba. On the other hand, gravitational waves from the merger of two objects create clear, strong signals like an orchestra playing together. This is because when two compact objects are about to merge, they dance in a tight orbit that creates gravitational waves with each turn. This rhythm of near-identical waves amplifies the signal to a level that can be detected. The new simulations showed that the rotating disks around collapsars can also emit gravitational waves that amplify together, very much like the orbiting compact objects in mergers.
Continue Reading this Simons Foundation article: https://www.simonsfoundation.org/2024/08/22/new-detectable-gravitational-wave-source-from-collapsing-stars-predicted-from-simulations/?utm_source=SimonsFoundation.org+Newsletter&utm_campaign=50cd97bedc-SF_NEWSLETTER_SEPTEMBER_2024&utm_medium=email&utm_term=0_01c00e64ea-50cd97bedc-392602897&mc_cid=50cd97bedc
ARTICLE TWO (2) | Astrophysicists Use Artificial Intelligence — AI to Precisely Calculate Universe’s Settings (September 2024)
Simons Foundation (article): https://www.simonsfoundation.org/2024/08/26/astrophysicists-use-ai-to-precisely-calculate-universes-settings/
The New Estimates of the Parameters that form the Basis of the Standard Model of Cosmology are far more precise than previous approaches using the same Galaxy Distribution Data.
The standard model of the universe relies on just six numbers. Using a new approach powered by artificial intelligence, researchers at the Flatiron Institute and their colleagues extracted information hidden in the distribution of galaxies to estimate the values of five of these so-called cosmological parameters with incredible precision.
The results were a significant improvement over the values produced by previous methods. Compared to conventional techniques using the same galaxy data, the approach yielded less than half the uncertainty for the parameter describing the clumpiness of the universe’s matter. The AI-powered method also closely agreed with estimates of the cosmological parameters based on observations of other phenomena, such as the universe’s oldest light.
The researchers present their method, the Simulation-Based Inference of Galaxies (or SimBIG), in a series of recent papers, including a new study published August 21 in Nature Astronomy.
Generating tighter constraints on the parameters while using the same data will be crucial to studying everything from the composition of dark matter to the nature of the dark energy driving the universe apart, says study co-author Shirley Ho, a group leader at the Flatiron Institute’s Center for Computational Astrophysics (CCA) in New York City. That’s especially true as new surveys of the cosmos come online over the next few years, she says.
“Each of these surveys costs hundreds of millions to billions of dollars,” Ho says. “The main reason these surveys exist is because we want to understand these cosmological parameters better. So if you think about it in a very practical sense, these parameters are worth tens of millions of dollars each. You want the best analysis you can to extract as much knowledge out of these surveys as possible and push the boundaries of our understanding of the universe.”
The six cosmological parameters describe the amount of ordinary matter, dark matter and dark energy in the universe and the conditions following the Big Bang, such as the opacity of the newborn universe as it cooled and whether mass in the cosmos is spread out or in big clumps. The parameters “are essentially the ‘settings’ of the universe that determine how it operates on the largest scales,” says Liam Parker, co-author of the Nature Astronomy study and a research analyst at the CCA.
One of the most important ways cosmologists calculate the parameters is by studying the clustering of the universe’s galaxies. Previously, these analyses only looked at the large-scale distribution of galaxies.
“We haven’t been able to go down to small scales,” says ChangHoon Hahn, an associate research scholar at Princeton University and lead author of the Nature Astronomy study. “For a couple of years now, we’ve known that there’s additional information there; we just didn’t have a good way of extracting it.”
Hahn proposed a way to leverage AI to extract that small-scale information. His plan had two phases. First, he and his colleagues would train an AI model to determine the values of the cosmological parameters based on the appearance of simulated universes. Then they’d show their model actual galaxy distribution observations.
Hahn, Ho, Parker and their colleagues trained their model by showing it 2,000 box-shaped universes from the CCA-developed Quijote simulation suite, with each universe created using different values for the cosmological parameters. The researchers even made the 2,000 universes appear like data generated by galaxy surveys — including flaws from the atmosphere and the telescopes themselves — to give the model realistic practice. “That’s a large number of simulations, but it’s a manageable amount,” Hahn says. “If you didn’t have the machine learning, you’d need hundreds of thousands.”
By ingesting the simulations, the model learned over time how the values of the cosmological parameters correlate with small-scale differences in the clustering of galaxies, such as the distance between individual pairs of galaxies. SimBIG also learned how to extract information from the bigger-picture arrangement of the universe’s galaxies by looking at three or more galaxies at a time and analyzing the shapes created between them, like long, stretched triangles or squat equilateral triangles.
Continue Reading this Simons Foundation article: https://www.simonsfoundation.org/2024/08/26/astrophysicists-use-ai-to-precisely-calculate-universes-settings/
ARTICLE THREE (3) | Hyped Signal of Decaying Dark Matter Vanishes in Updated Analysis (September 2024)
In 2014, scientists observed X-ray activity from distant galaxies that was thought to be the first evidence of dark matter decay — a landmark discovery that could significantly advance efforts to characterize this puzzling substance. However, a new study from the Flatiron Institute and collaborators suggests that imperfect analysis methods used to detect the activity — called the 3.5 keV line — likely produced a phantom signal.
In 2014, astrophysicists caught a glimpse of what they thought was their white whale: evidence of the nature of the mysterious and elusive dark matter that makes up 85 percent of the universe’s material. They spotted X-ray activity thought to result from decaying dark matter, as typical matter would not have been able to produce such a signal. With this exciting discovery, a window seemed to have finally opened into dark matter’s secrets.
The problem, however, is that according to new research, the signal (called the 3.5 keV line) probably never existed in the first place. By re-creating the original studies’ analysis techniques and applying new, more comprehensive tools, a team of astrophysicists concluded that the 3.5 keV line originally arose from flaws in data analysis. The team reports their findings in the April 1 issue of The Astrophysical Journal.
“This is an important result because we’re showing that these previous methodologies used to study dark matter decay may not be optimal and could be giving spurious results,” says study lead author Christopher Dessert, a postdoctoral fellow at the Flatiron Institute’s Center for Computational Astrophysics and New York University.
Dessert co-authored the study with Benjamin Safdi and Yujin Park of the University of California, Berkeley and Lawrence Berkeley National Laboratory, as well as Joshua Foster of the Massachusetts Institute of Technology.
Continue Reading this Simons Foundation article: https://www.simonsfoundation.org/2024/08/19/hyped-signal-of-decaying-dark-matter-vanishes-in-updated-analysis/?utm_source=SimonsFoundation.org+Newsletter&utm_campaign=50cd97bedc-SF_NEWSLETTER_SEPTEMBER_2024&utm_medium=email&utm_term=0_01c00e64ea-50cd97bedc-392602897&mc_cid=50cd97bedc
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