Berkeley Lab Computing Resources Enable Deeper Understanding of Supernovae Explosions
March 23, 2022
By Keri Troutman
Contact: [email protected]
An international team of astrophysics researchers recently made history by recording the earliest post-explosion detection of a Type Ia supernova – a thermonuclear explosion of white dwarf stars. Using cosmological models developed by scientists at Lawrence Berkeley National Laboratory (Berkeley Lab), in conjunction with supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), the researchers were able to interpret cosmological observations never seen before. The combination of advances in detection technology and modeling methods made this possible.
Their results, published in Nature Astronomy, inform a deeper understanding of the supernovae explosion process. The data that the team collected show a concentration of iron and other heavy elements in the outermost portion of debris from the explosion, which was revealed by observations of a rapid reddening of the supernova’s light in the first few hours of data. This means that thermonuclear explosions are initiated by either nuclear burning on the surface of an exploding star or an extreme mixing of the exploding stellar material – an important milestone in understanding how supernovae explode.
This study was led by the University of Toronto’s Yuan Qi Ni, a graduate student in the David A. Dunlap Department of Astronomy and Astrophysics, along with University of Toronto Professors and Dunlap Associates Dae-Sik Moon and Maria Drout. The team used the Korea Microlensing Telescope Network, which consists of telescopes in Chile, South Africa, and Australia. With a 24-hour continuous coverage of the night sky, they were able to discover the SN2018aoz supernova just one hour after its birth. They then monitored it for more than a year, using data collected with telescopes around the world and in space.
“Discovering supernovae during their infant phase is really challenging,” Moon said. “The multiple colors seen revealed features never seen before, which guided us to better understand the supernova explosion processes.”
Type Ia supernovae play a central role in the chemical evolution of the Universe and are an important measure of cosmological distances. Despite extensive efforts to obtain natal information from their earliest signals, observations have thus far failed to identify how the majority of them explode. Detection at this early phase is a feat of the efforts over the last decade of pushing telescopes to lower luminosities and higher cadence to catch things earlier, said Abigail Polin, a postdoctoral research fellow at Carnegie Observatories and Caltech who is a co-author on the Nature Astronomy paper.
Polin did her graduate work in Berkeley Lab’s Computational Cosmology Center with Peter Nugent, senior scientist and division deputy for science engagement for Berkeley Lab’s Applied Mathematics and Computational Research Division. She also won a NERSC Early Career researcher award last year for her thesis work with Nugent.
Polin’s research at Berkeley Lab was focused on modeling a specific pathway to type Ia supernovae, known as the double detonation mechanism. It is this method that allowed researchers to see the unique features inherent in this specific type Ia supernova. The data that the team collected showed a concentration of iron and other heavy elements in the outermost portion of debris from the explosion, suggesting that thermonuclear explosions are initiated by either nuclear burning on the surface of an exploding star or an extreme mixing of the exploding stellar material.
“The general consensus was that if we probe an Ia this early we would just see a smooth rise in the light curve, as is typical for what had previously been the earliest observations,” said Polin. “So, it was surprising that for the first one we probed this early we saw something different than that – we saw this natal bump that now we’re trying to explain.”
Modeling the Explosion Mechanism
The researchers used NERSC’s Cori system to perform all of the modeling efforts for this project, first performing hydrodynamic simulations of the actual explosion with Castro, an astrophysical simulation code also developed at Berkeley Lab. They then turned to Polin’s work to model the infancy of this particular supernovae.
Polin spent her Ph.D. studying this explosion mechanism, building a database of explosion models and then running simulations to apply the models to real-life supernovae observations. She published her suite of models. Now when a supernova is observed with any of the signatures her models point to, it triggers a contact.
“We get a call and we either provide a model we’ve already written that’s in the database or, if it is interesting enough like this one, we create custom models to probe more deeply,” said Polin. “With this supernova, we had to push our models to even earlier times than we had done before. Normally we start our calculations at about a quarter of a day after explosion and that wasn’t early enough for this.”
When Berkeley Lab first got involved in supernovae research decades ago, supernovae were basically used as “blunt instruments for cosmology,” said Nugent, yet now the research is aimed at trying to get really precise measurements, interweaving theory and observation. “The work that Abi [Polin] has done in her thesis is a direct path to understanding what we’re seeing with supernovae,” he said. “If we can make this breakthrough and separate these things out, we learn even more about supernovae and about how to make better probes for cosmology.”
“The game now is to catch more of these supernovae early to figure out if this is a normal feature or if we just got lucky,” said Polin. “For me, events like this remind me of why I love what I do. I can sit here at a computer and model explosions all day, but it is the times when we actually see things in the real world that remind me of why this is exciting.”
NERSC is a U.S. Department of Energy Office of Science user facility.
For more information about this research, see this University of Toronto news release.
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