High School Student Discovers 1.5M New Astronomical Objects by Developing an AI Algorithm (smithsonianmag.com)
(Saturday April 19, 2025 @05:34PM (EditorDavid)
from the sky-and-telescopes dept.)
- Reference: 0177065945
- News link: https://science.slashdot.org/story/25/04/19/1353245/high-school-student-discovers-15m-new-astronomical-objects-by-developing-an-ai-algorithm
- Source link: https://www.smithsonianmag.com/smart-news/high-school-student-discovers-1-5-million-potential-new-astronomical-objects-by-developing-an-ai-algorithm-180986429/
For combining machine learning with astronomy, high school senior Matteo Paz won $250,000 in the Regeneron Science Talent Search, [1]reports Smithsonian magazine :
> The young scientist's tool processed 200 billion data entries from NASA's [2]now-retired Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) telescope. His model revealed 1.5 million previously unknown potential celestial bodies.... [H]e worked on an A.I. model that sorted through the raw data in search of tiny changes in infrared radiation, which could indicate the presence of variable objects.
Working with a mentor at the Planet Finder Academy at Caltech, Paz eventually flagged 1.5 million potential new objects, accoridng to the article, including supernovas and black holes.
And that mentor says other Caltech researchers are using Paz's catalog of potential variable objects to study binary star systems.
Thanks to long-time Slashdot reader [3]schwit1 for sharing the article.
[1] https://www.smithsonianmag.com/smart-news/high-school-student-discovers-1-5-million-potential-new-astronomical-objects-by-developing-an-ai-algorithm-180986429/
[2] https://www.smithsonianmag.com/smart-news/nasa-retires-orbiting-telescope-that-charted-asteroids-for-over-a-decade-180984872/
[3] https://www.slashdot.org/~schwit1
> The young scientist's tool processed 200 billion data entries from NASA's [2]now-retired Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) telescope. His model revealed 1.5 million previously unknown potential celestial bodies.... [H]e worked on an A.I. model that sorted through the raw data in search of tiny changes in infrared radiation, which could indicate the presence of variable objects.
Working with a mentor at the Planet Finder Academy at Caltech, Paz eventually flagged 1.5 million potential new objects, accoridng to the article, including supernovas and black holes.
And that mentor says other Caltech researchers are using Paz's catalog of potential variable objects to study binary star systems.
Thanks to long-time Slashdot reader [3]schwit1 for sharing the article.
[1] https://www.smithsonianmag.com/smart-news/high-school-student-discovers-1-5-million-potential-new-astronomical-objects-by-developing-an-ai-algorithm-180986429/
[2] https://www.smithsonianmag.com/smart-news/nasa-retires-orbiting-telescope-that-charted-asteroids-for-over-a-decade-180984872/
[3] https://www.slashdot.org/~schwit1
Re: (Score:3)
by war4peace ( 1628283 )
Um, what?
Machine Learning uses algorithms for training.
Re: (Score:1)
by registrations_suck ( 1075251 )
What we currently call "artificially intelligent" isn't artificially intelligent either.
Smithsonian Magazine? (Score:3, Funny)
by 93 Escort Wagon ( 326346 )
I'm surprised that hasn't been renamed by the current administration yet.
Re: (Score:2)
by DaveyJJ ( 1198633 )
> I'm surprised that hasn't been renamed by the current administration yet.
It won't ever be. We're in the process of deporting him elsewhere because we don't need smarty-pants around here. He's next after we remove the Pennsylvania-born doctor from her New England practice and ship her elsewhere. "Lisa Anderson" ... a foreign name if I ever heard one. [1]https://www.msn.com/en-ca/news... [msn.com]
[1] https://www.msn.com/en-ca/news/world/american-doctor-with-full-us-citizenship-told-to-leave-the-country/ar-AA1D8fAN
I wonder.. (Score:2)
.. how many of those objects are AI hallucinations?
Re: (Score:3)
He is not using a LLM, he is using Machine Learning Classification.
Of course, the output validity really depends on how correct the original data is, how well the machine learning method is implemented (Eager Learning, Lazy Learning and all that) and so on.
The model can be tuned, of course. There will be many false positives, of course, but these will exist no matter whether the data is processed manually or through a model. They all need to be validated anyway, regardless of method.