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Astronomers are utilizing AI to measure the expansion of the universe. Two studies led by Maria Dainotti, a visiting professor with UNLV's Nevada Center for Astrophysics and assistant professor at the National Astronomical Observatory of Japan (NAOJ), applied machine learning models to enhance distance measurements for gamma-ray bursts (GRBs). GRBs are the most luminous explosions in the univers by Riko Seibo
Tokyo, Japan (SPX) Jun 12, 2024
Astronomers are utilizing AI to measure the expansion of the universe. Two studies led by Maria Dainotti, a visiting professor with UNLV's Nevada Center for Astrophysics and assistant professor at the National Astronomical Observatory of Japan (NAOJ), applied machine learning models to enhance distance measurements for gamma-ray bursts (GRBs). GRBs are the most luminous explosions in the universe.
GRBs release as much energy in seconds as the sun does in its entire lifetime. Their brightness allows them to be observed at various distances, including the edge of the visible universe. This helps astronomers study the oldest and most distant stars. However, only a small percentage of known GRBs have the necessary observational characteristics to calculate their distance accurately.
Dainotti's teams used data from NASA's Neil Gehrels Swift Observatory and machine learning models to estimate the distances of GRBs with unknown proximities. This helps scientists understand star evolution and the frequency of GRBs over time and space.
"This research pushes forward the frontier in both gamma-ray astronomy and machine learning," said Dainotti. "Follow-up research and innovation will help us achieve even more reliable results and enable us to answer some of the most pressing cosmological questions, including the earliest processes of our universe and how it has evolved over time."
In one study, Dainotti and Aditya Narendra, a doctoral student at Poland's Jagiellonian University, used machine learning to measure the distance of GRBs observed by the Swift UltraViolet/Optical Telescope (UVOT) and ground-based telescopes like the Subaru Telescope. The research was published Feb. 8 on arXiv.
"The outcome of this study is so precise that we can determine using predicted distance the number of GRBs in a given volume and time (called the rate), which is very close to the actual observed estimates," said Narendra.
Another study led by Dainotti and international collaborators used data from NASA's Swift X-ray Telescope (XRT) afterglows from long GRBs to measure distances. Long GRBs occur when a massive star explodes in a supernova, while short GRBs happen when neutron stars merge. This method, published Feb. 26 in The Astrophysical Journal, Supplement Series, estimated the distance of 154 long GRBs and significantly increased the population of known distances for this type of burst.
A third study, published Feb. 21 in the Astrophysical Journal Letters and led by Stanford University astrophysicist Vahe Petrosian and Dainotti, used Swift X-ray data to reveal that the GRB rate at small relative distances does not follow the star formation rate. "This opens the possibility that long GRBs at small distances may be generated not by a collapse of massive stars, but rather by the fusion of very dense objects like neutron stars," said Petrosian.
With support from NASA's Swift Observatory Guest Investigator program, Dainotti and colleagues are working to make the machine learning tools publicly available through an interactive web application.
Research Report:Gamma-Ray Bursts as Distance Indicators by a Statistical Learning Approach
Related Links
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