AI Might Help Astronomers Better Understand Gamma-Ray Bursts
Gamma-ray bursts are the most powerful explosions in the universe and with the help of AI, astronomers hope to learn more. Compared to supernovas, gamma-ray bursts are on a level of their own. As it stands, these explosions come in two types, long-duration and short-duration, and there is still a lot to be known. Much of that has to do with the sheer overlap between the two main types of gamma-ray bursts. The short gamma-ray bursts last for around two seconds and account for 30% of all bursts detected. Long-duration bursts on the other hand are brighter by a larger factor. Because of the time and brightness difference, a significant number of detected bursts bleed between two major groupings.
First, a quick intro into what are gamma-ray bursts. In simplest terms, they are the mergers of extremely dense objects, such as neutron stars, which causes the universe’s most powerful explosion. It is believed that short gamma-ray bursts are created by the aforementioned mergers of neutron stars. On the other hand, it believes unique and exotic supernovas cause long-duration gamma-ray bursts. First detected in 1967 by the Vela satellites, gamma-ray bursts shocked the astronomy community with theoretical models created in an attempt to explain their existence. It wasn’t until 1997 with the detection of X-Ray and afterglows that directly measured their redshifts using spectroscopy. This gave the community both distance and energy outputs. With that information, it became clear that these events occur far outside the Milky Way Galaxy.
According to a paper from researchers at Cornell University, they are proposing a new mechanism for distinguishing these two classes. The way it works is that they employed machine learning algorithms trained on existing data sets (GRB Big Table and Greiner’s GRB catalog). From there, they use computer simulations to find the key distinguishing characteristics between short and long gamma-ray bursts. These features are divided into three sub-groups, prompt emission, afterglow, and host galaxy. In their findings, the team was able to cleanly separate the populations of observations even when the duration time of the blast was right at the boundary.
The team hopes that this supervised machine learning tool will make it possible to easily classify future observations. This then can be used to refine our understanding of the physical mechanisms behind the largest explosions known in the universe.
If you’re curious about what gamma-ray busts might look like, NASA created a fun animation which you can view below:
Originally posted on OpenDataScience.com
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