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What Can AI Learn About the Universe? – Universe Today

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Artificial intelligence and machine learning have become ubiquitous, with applications ranging from data analysis, cybersecurity, pharmaceutical development, music composition, and artistic renderings. In recent years, large language models (LLMs) have also emerged, adding human interaction and writing to the long list of applications. This includes ChatGPT, an LLM that has had a profound impact since it was introduced less than two years ago. This application has sparked considerable debate (and controversy) about AI’s potential uses and implications.

Astronomy has also benefitted immensely, where machine learning is used to sort through massive volumes of data to look for signs of planetary transits, correct for atmospheric interference, and find patterns in the noise. According to an international team of astrophysicists, this may just be the beginning of what AI could do for astronomy. In a recent study, the team fine-tuned a Generative Pre-trained Transformer (GPT) model using observations of astronomical objects. In the process, they successfully demonstrated that GPT models can effectively assist with scientific research.

The study was conducted by the International Center for Relativistic Astrophysics Network (ICRANet), an international consortium made up of researchers from the International Center for Relativistic Astrophysics (ICRA), the National Institute for Astrophysics (INAF), the University of Science and Technology of China, the Chinese Academy of Sciences Institute of High Energy Physics (CAS-IHEP), the University of Padova, the Isfahan University of Technology, and the University of Ferrera. The preprint of their paper, “Test of Fine-Tuning GPT by Astrophysical Data,” recently appeared online.

Illustration of an active quasar. New research shows AI can identify and classify them. Credit: ESO/M. Kornmesser

As mentioned, astronomers rely extensively on machine learning algorithms to sort through the volumes of data obtained by modern telescopes and instruments. This practice began about a decade ago and has since grown by leaps and bounds to the point where AI has been integrated into the entire research process. As ICRA President and the study’s lead author Yu Wang told Universe Today via email:

“Astronomy has always been driven by data and astronomers are some of the first scientists to adopt and employ machine learning. Now, machine learning has been integrated into the entire astronomical research process, from the manufacturing and control of ground-based and space-based telescopes (e.g., optimizing the performance of adaptive optics systems, improving the initiation of specific actions (triggers) of satellites under certain conditions, etc.), to data analysis (e.g., noise reduction, data imputation, classification, simulation, etc.), and the establishment and validation of theoretical models (e.g., testing modified gravity, constraining the equation of state of neutron stars, etc.).”

Data analysis remains the most common among these applications since it is the easiest area where machine learning can be integrated. Traditionally, dozens of researchers and hundreds of citizen scientists would analyze the volumes of data produced by an observation campaign. However, this is not practical in an age where modern telescopes are collecting terabytes of data daily. This includes all-sky surveys like the Very Large Array Sky Survey (VLASS) and the many phases conducted by the Sloan Digital Sky Survey (SDSS).

To date, LLMs have only been applied sporadically to astronomical research, given that they are a relatively recent creation. But according to proponents like Wang, it has had a tremendous societal impact and has a lower-limit potential equivalent to an “Industrial Revolution.” As for the upper limit, Wang predicts that that could range considerably and could perhaps result in humanity’s “enlightenment or destruction.” However, unlike the Industrial Revolution, the pace of change and integration is far more rapid for AI, raising questions about how far its adoption will go.

The Sloan Digital Sky Survey telescope stands out against the breaktaking backdrop of the Sacramento Mountains. 234 stars out of the Sloan's catalogue of over 2.5 million stars are producing an unexplained pulsed signal. Image: SDSS, Fermilab Visual Media Services
The Sloan Digital Sky Survey telescope stands out against the breathtaking backdrop of the Sacramento Mountains. Credit: SDSS/Fermilab Visual Media Services

To determine its potential for the field of astronomy, said Wang, he and his colleagues adopted a pre-trained GPT model and fine-tuned it to identify astronomical phenomena:

“OpenAI provides pre-trained models, and what we did is fine-tuning, which involves altering some parameters based on the original model, allowing it to recognize astronomical data and calculate results from this data. This is somewhat like OpenAI providing us with an undergraduate student, whom we then trained to become a graduate student in astronomy. 

“We provided limited data with modest resolution and trained the GPT fewer times compared to normal models. Nevertheless, the outcomes are impressive, achieving an accuracy of about 90%. This high level of accuracy is attributable to the robust foundation of the GPT, which already understands data processing and possesses logical inference capabilities, as well as communication skills.”

To fine-tune their model, the team introduced observations of various astronomical phenomena derived from various catalogs. This included 2000 samples of quasars, galaxies, stars, and broad absorption line (BAL) quasars from the SDSS (500 each). They also integrated observations of short and long gamma-ray bursts (GRBs), galaxies, stars, and black hole simulations. When tested, their model successfully classified different phenomena, distinguished between types of quasars, inferred their distance based on redshift, and measured the spin and inclination of black holes.

“This work at least demonstrates that LLMs are capable of processing astronomical data,” said Wang. “Moreover, the ability of a model to handle various types of astronomical data is a capability not possessed by other specialized models. We hope that LLMs can integrate various kinds of data and then identify common underlying principles to help us understand the world. Of course, this is a challenging task and not one that astronomers can accomplish alone.”

The Vera Rubin Observatory at twilight on April 2021. It’s been a long wait, but the observatory should see first light later this year. Credit: Rubin Obs/NSF/AURA

Of course, the team acknowledges that the dataset they experimented with was very small compared to the data output of modern observatories. This is particularly true of next-generation facilities like the Vera C. Rubin Observatory, which recently received its LSST camera, the largest digital camera in the world! Once Rubin is operational, it will conduct the ten-year Legacy Survey of Space and Time (LSST), which is expected to yield 15 terabytes of data per night! Satisfying the demands of future campaigns, says Wang, will require improvements and collaboration between observatories and professional AI companies.

Nevertheless, it’s a foregone conclusion that there will be more LLM applications for astronomy in the near future. Not only is this a likely development, but a necessary one considering the sheer volumes of data astronomical studies are generating today. And since this is likely to increase exponentially in the near future, AI will likely become indispensable to the field of study.

Further Reading: arXiv

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Here’s how Helene and other storms dumped a whopping 40 trillion gallons of rain on the South

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More than 40 trillion gallons of rain drenched the Southeast United States in the last week from Hurricane Helene and a run-of-the-mill rainstorm that sloshed in ahead of it — an unheard of amount of water that has stunned experts.

That’s enough to fill the Dallas Cowboys’ stadium 51,000 times, or Lake Tahoe just once. If it was concentrated just on the state of North Carolina that much water would be 3.5 feet deep (more than 1 meter). It’s enough to fill more than 60 million Olympic-size swimming pools.

“That’s an astronomical amount of precipitation,” said Ed Clark, head of the National Oceanic and Atmospheric Administration’s National Water Center in Tuscaloosa, Alabama. “I have not seen something in my 25 years of working at the weather service that is this geographically large of an extent and the sheer volume of water that fell from the sky.”

The flood damage from the rain is apocalyptic, meteorologists said. More than 100 people are dead, according to officials.

Private meteorologist Ryan Maue, a former NOAA chief scientist, calculated the amount of rain, using precipitation measurements made in 2.5-mile-by-2.5 mile grids as measured by satellites and ground observations. He came up with 40 trillion gallons through Sunday for the eastern United States, with 20 trillion gallons of that hitting just Georgia, Tennessee, the Carolinas and Florida from Hurricane Helene.

Clark did the calculations independently and said the 40 trillion gallon figure (151 trillion liters) is about right and, if anything, conservative. Maue said maybe 1 to 2 trillion more gallons of rain had fallen, much if it in Virginia, since his calculations.

Clark, who spends much of his work on issues of shrinking western water supplies, said to put the amount of rain in perspective, it’s more than twice the combined amount of water stored by two key Colorado River basin reservoirs: Lake Powell and Lake Mead.

Several meteorologists said this was a combination of two, maybe three storm systems. Before Helene struck, rain had fallen heavily for days because a low pressure system had “cut off” from the jet stream — which moves weather systems along west to east — and stalled over the Southeast. That funneled plenty of warm water from the Gulf of Mexico. And a storm that fell just short of named status parked along North Carolina’s Atlantic coast, dumping as much as 20 inches of rain, said North Carolina state climatologist Kathie Dello.

Then add Helene, one of the largest storms in the last couple decades and one that held plenty of rain because it was young and moved fast before it hit the Appalachians, said University of Albany hurricane expert Kristen Corbosiero.

“It was not just a perfect storm, but it was a combination of multiple storms that that led to the enormous amount of rain,” Maue said. “That collected at high elevation, we’re talking 3,000 to 6000 feet. And when you drop trillions of gallons on a mountain, that has to go down.”

The fact that these storms hit the mountains made everything worse, and not just because of runoff. The interaction between the mountains and the storm systems wrings more moisture out of the air, Clark, Maue and Corbosiero said.

North Carolina weather officials said their top measurement total was 31.33 inches in the tiny town of Busick. Mount Mitchell also got more than 2 feet of rainfall.

Before 2017’s Hurricane Harvey, “I said to our colleagues, you know, I never thought in my career that we would measure rainfall in feet,” Clark said. “And after Harvey, Florence, the more isolated events in eastern Kentucky, portions of South Dakota. We’re seeing events year in and year out where we are measuring rainfall in feet.”

Storms are getting wetter as the climate change s, said Corbosiero and Dello. A basic law of physics says the air holds nearly 4% more moisture for every degree Fahrenheit warmer (7% for every degree Celsius) and the world has warmed more than 2 degrees (1.2 degrees Celsius) since pre-industrial times.

Corbosiero said meteorologists are vigorously debating how much of Helene is due to worsening climate change and how much is random.

For Dello, the “fingerprints of climate change” were clear.

“We’ve seen tropical storm impacts in western North Carolina. But these storms are wetter and these storms are warmer. And there would have been a time when a tropical storm would have been heading toward North Carolina and would have caused some rain and some damage, but not apocalyptic destruction. ”

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Follow AP’s climate coverage at https://apnews.com/hub/climate

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Follow Seth Borenstein on Twitter at @borenbears

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Associated Press climate and environmental coverage receives support from several private foundations. See more about AP’s climate initiative here. The AP is solely responsible for all content.

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‘Big Sam’: Paleontologists unearth giant skull of Pachyrhinosaurus in Alberta

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It’s a dinosaur that roamed Alberta’s badlands more than 70 million years ago, sporting a big, bumpy, bony head the size of a baby elephant.

On Wednesday, paleontologists near Grande Prairie pulled its 272-kilogram skull from the ground.

They call it “Big Sam.”

The adult Pachyrhinosaurus is the second plant-eating dinosaur to be unearthed from a dense bonebed belonging to a herd that died together on the edge of a valley that now sits 450 kilometres northwest of Edmonton.

It didn’t die alone.

“We have hundreds of juvenile bones in the bonebed, so we know that there are many babies and some adults among all of the big adults,” Emily Bamforth, a paleontologist with the nearby Philip J. Currie Dinosaur Museum, said in an interview on the way to the dig site.

She described the horned Pachyrhinosaurus as “the smaller, older cousin of the triceratops.”

“This species of dinosaur is endemic to the Grand Prairie area, so it’s found here and nowhere else in the world. They are … kind of about the size of an Indian elephant and a rhino,” she added.

The head alone, she said, is about the size of a baby elephant.

The discovery was a long time coming.

The bonebed was first discovered by a high school teacher out for a walk about 50 years ago. It took the teacher a decade to get anyone from southern Alberta to come to take a look.

“At the time, sort of in the ’70s and ’80s, paleontology in northern Alberta was virtually unknown,” said Bamforth.

When paleontogists eventually got to the site, Bamforth said, they learned “it’s actually one of the densest dinosaur bonebeds in North America.”

“It contains about 100 to 300 bones per square metre,” she said.

Paleontologists have been at the site sporadically ever since, combing through bones belonging to turtles, dinosaurs and lizards. Sixteen years ago, they discovered a large skull of an approximately 30-year-old Pachyrhinosaurus, which is now at the museum.

About a year ago, they found the second adult: Big Sam.

Bamforth said both dinosaurs are believed to have been the elders in the herd.

“Their distinguishing feature is that, instead of having a horn on their nose like a triceratops, they had this big, bony bump called a boss. And they have big, bony bumps over their eyes as well,” she said.

“It makes them look a little strange. It’s the one dinosaur that if you find it, it’s the only possible thing it can be.”

The genders of the two adults are unknown.

Bamforth said the extraction was difficult because Big Sam was intertwined in a cluster of about 300 other bones.

The skull was found upside down, “as if the animal was lying on its back,” but was well preserved, she said.

She said the excavation process involved putting plaster on the skull and wooden planks around if for stability. From there, it was lifted out — very carefully — with a crane, and was to be shipped on a trolley to the museum for study.

“I have extracted skulls in the past. This is probably the biggest one I’ve ever done though,” said Bamforth.

“It’s pretty exciting.”

This report by The Canadian Press was first published Sept. 25, 2024.

The Canadian Press. All rights reserved.

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The ancient jar smashed by a 4-year-old is back on display at an Israeli museum after repair

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TEL AVIV, Israel (AP) — A rare Bronze-Era jar accidentally smashed by a 4-year-old visiting a museum was back on display Wednesday after restoration experts were able to carefully piece the artifact back together.

Last month, a family from northern Israel was visiting the museum when their youngest son tipped over the jar, which smashed into pieces.

Alex Geller, the boy’s father, said his son — the youngest of three — is exceptionally curious, and that the moment he heard the crash, “please let that not be my child” was the first thought that raced through his head.

The jar has been on display at the Hecht Museum in Haifa for 35 years. It was one of the only containers of its size and from that period still complete when it was discovered.

The Bronze Age jar is one of many artifacts exhibited out in the open, part of the Hecht Museum’s vision of letting visitors explore history without glass barriers, said Inbal Rivlin, the director of the museum, which is associated with Haifa University in northern Israel.

It was likely used to hold wine or oil, and dates back to between 2200 and 1500 B.C.

Rivlin and the museum decided to turn the moment, which captured international attention, into a teaching moment, inviting the Geller family back for a special visit and hands-on activity to illustrate the restoration process.

Rivlin added that the incident provided a welcome distraction from the ongoing war in Gaza. “Well, he’s just a kid. So I think that somehow it touches the heart of the people in Israel and around the world,“ said Rivlin.

Roee Shafir, a restoration expert at the museum, said the repairs would be fairly simple, as the pieces were from a single, complete jar. Archaeologists often face the more daunting task of sifting through piles of shards from multiple objects and trying to piece them together.

Experts used 3D technology, hi-resolution videos, and special glue to painstakingly reconstruct the large jar.

Less than two weeks after it broke, the jar went back on display at the museum. The gluing process left small hairline cracks, and a few pieces are missing, but the jar’s impressive size remains.

The only noticeable difference in the exhibit was a new sign reading “please don’t touch.”

The Canadian Press. All rights reserved.

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