Astronomers are now using AI, quite literally, to measure the expansion of our universe. In recent 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), multiple machine learning models were incorporated to add a new level of precision to distance measurements for gamma-ray bursts (GRBs) – the most luminous and violent explosions in the universe. This is according to a press release by University of Nevada, Las Vegas (UNLV).
Chasing the distant stars
In just a few seconds, GRBs release the same amount of energy our sun releases in its entire lifetime. Because they are so bright, GRBs can be observed at multiple distances – including at the edge of the visible universe – and aid astronomers in their quest to chase the oldest and most distant stars. But, due to the limits of current technology, only a small percentage of known GRBs have all of the observational characteristics needed to aid astronomers in calculating how far away they occurred.
Dainotti and her teams combined GRB data from NASA’s Neil Gehrels Swift Observatory with multiple machine learning models to overcome the limitations of current observational technology and, more precisely, estimate the proximity of GRBs for which the distance is unknown. Because GRBs can be observed both far away and at relatively close distances, knowing where they occurred can help scientists understand how stars evolve and how many GRBs can occur in a given space and time.
The full press release on unlv.edu
Lund Professor: ”A rewarding collaborative project”
Malgorzata Bogdan is a Professor of Statistics at Lund University School of Economics and Management. She has been collaborating with Maria Dainotti for a period of ten years. She comments on the project:
”This is the final part of our long collaboration with Prof. Dainotti on predicting the redshifts of distant astronomical objects. Compared to our previous joint research on supernovae, the size of the GRB training set is much smaller, making the redshift predictions much more challenging. It is gratifying that the precision we obtained is sufficient for reliable estimation of cosmological parameters, which is useful for investigating the evolution of the Universe.”
”This was one of my most rewarding collaborative research projects. My primary role was to consult on the choice of statistical methods and the interpretation of the results. The task was made easy due to the excellent mathematical and computer science preparation, professional standards, and commitment of Professor Dainotti’s team”, Malgorzata Bogdan concludes.
Research articles
- Gamma-Ray Bursts as Distance Indicators by a Statistical Learning Approach – The Astrophysical Journal Letters
- Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model – The Astrophysical Journal, Supplement Series
Progenitors of Low-redshift Gamma-Ray Bursts – Astrophysical Journal Letters
All articles are open access.
