Separating gamma-ray bursts: Students make critical breakthrough

Date:
July 17, 2020
Source:
University of Copenhagen
Summary:
By applying a machine-learning algorithm, scientists have developed
a method to classify all gamma-ray bursts (GRBs), rapid highly
energetic explosions in distant galaxies, without needing to
find an afterglow - by which GRBs are presently categorized. This
breakthrough, initiated by first-year B.Sc. students, may prove
key in finally discovering the origins of these mysterious bursts.



FULL STORY ==========================================================================
By applying a machine-learning algorithm, scientists at the Niels
Bohr Institute, University of Copenhagen, have developed a method to
classify all gamma-ray bursts (GRBs), rapid highly energetic explosions in distant galaxies, without needing to find an afterglow -- by which GRBs
are presently categorized. This breakthrough, initiated by first-year
B.Sc. students, may prove key in finally discovering the origins of
these mysterious bursts. The result is now published in Astrophysical
Journal Letters.


==========================================================================
Ever since gamma-ray bursts (GRBs) were accidentally picked up by Cold
War satellites in the 70s, the origin of these rapid bursts have been
a significant puzzle. Although many astronomers agree that GRBs can be
divided into shorter (typically less than 1 second) and longer (up to a
few minutes) bursts, the two groups are overlapping. It has been thought
that longer bursts might be associated with the collapse of massive
stars, while shorter bursts might instead be caused by the merger of
neutron stars. However, without the ability to separate the two groups
and pinpoint their properties, it has been impossible to test these ideas.

So far, it has only been possible to determine the type of a GRB about 1%
of the time, when a telescope was able to point at the burst location
quickly enough to pick up residual light, called an afterglow. This
has been such a crucial step that astronomers have developed worldwide
networks capable of interrupting other work and repointing large
telescopes within minutes of the discovery of a new burst. One GRB
was even detected by the LIGO Observatory using gravitational waves,
for which the team was awarded the 2017 Nobel Prize.

Breakthrough achieved using machine-learning algorithm Now, scientists
at the Niels Bohr Institute have developed a method to classify all
GRBs without needing to find an afterglow. The group, led by first-year
B.Sc. Physics students Johann Bock Severin, Christian Kragh Jespersen and
Jonas Vinther, applied a machine-learning algorithm to classify GRBs. They identified a clean separation between long and short GRB's. Their work,
carried out under the supervision of Charles Steinhardt, will bring
astronomers a step closer to understanding GRB's.

This breakthrough may prove the key to finally discovering the origins
of these mysterious bursts. As Charles Steinhardt, Associate Professor
at the Cosmic Dawn Center of the Niels Bohr Institute explains, "Now
that we have two complete sets available, we can start exploring the differences between them.

So far, there had not been a tool to do that." From algorithm to
visual map


========================================================================== Instead of using a limited set of summary statistics, as was typically
done until then, the students decided to encode all available information
on GRB's using the machine learning algorithm t-SNE. The t-distributed Stochastic neighborhood embedding algorithm takes complex high-dimensional
data and produces a simplified and visually accessible map. It does so
without interfering with the structure of the dataset. "The unique thing
about this approach," explains Christian Kragh Jespersen, "is that t-SNE doesn't force there to be two groups. You let the data speak for itself
and tell you how it should be classified." Shining light on the data The preparation of the feature space -- the input you give the algorithm --
was the most challenging part of the project, says Johann Bock Severin.

Essentially, the students had to prepare the dataset in such a way that
its most important features would stand out. "I like to compare it to
hanging your data points from the ceiling in a dark room," explains
Christian Kragh Jespersen. "Our main problem was to figure out from what direction we should shine light on the data to make the separations
visible." "Step 0 in understanding GRB's" The students explored the
t-SNE machine-learning algorithm as part of their 1st Year project, a 1st
year course in the Bachelor of Physics. "By the time we got to the end
of the course, it was clear we had quite a significant result," their supervisor Charles Steinhardt says. The students' mapping of the t-SNE
cleanly divides all GRB's from the Swift observatory into two groups.

Importantly, it classifies GRB's that previously were difficult to
classify.

"This essentially is step 0 in understanding GRB's," explains
Steinhardt. "For the first time, we can confirm that shorter and longer
GRB's are indeed completely separate things." Without any prior
theoretical background in astronomy, the students have discovered a
key piece of the puzzle surrounding GRB's. From here, astronomers can
start to develop models to identify the characteristics of these two
separate classes.


========================================================================== Story Source: Materials provided by University_of_Copenhagen. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Christian K. Jespersen, Johann B. Severin, Charles L. Steinhardt,
Jonas
Vinther, Johan P. U. Fynbo, Jonatan Selsing, Darach Watson. An
Unambiguous Separation of Gamma-Ray Bursts into Two Classes from
Prompt Emission Alone. The Astrophysical Journal, 2020; 896 (2):
L20 DOI: 10.3847/2041-8213/ab964d ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200717120142.htm

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