Unravelling colliding black holes just got a boost, thanks to simulation tools
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Aaron M. Geller/Northwestern CIERA & NUIT-RCS; ESO/S. Brunier
Black holes are some of the most fascinating celestial bodies in the universe. Their gravitational fields are so strong that even light cannot escape them. One of the ways in which they are formed is when a massive star collapses, resulting in a stellar-mass black hole.
In 2015, the Laser Interferometer Gravitational-wave Observatory (LIGO) discovered gravitational waves (ripples through spacetime) from two colliding black holes. This groundbreaking discovery has prompted astrophysicists to study their origin and formation.
Now, a team of scientists has used POSYDON software to investigate the coalescing of binary black holes. The international team of scientists from the University of Geneva (UNIGE), Northwestern University, and the University of Florida have challenged earlier hypotheses by predicting the presence of the existence of merging massive black hole binaries with a mass of 30 times that of the Sun
What is POSYDON?
"As it is impossible to directly observe the formation of merging binary black holes, it is necessary to rely on simulations that reproduce their observational properties. We do this by simulating the binary-star systems from their birth to the formation of the binary black hole systems," said Simone Bavera, the lead author of the study.
POpulation SYnthesis with Detailed binary-evolution simulatiONs, or POSYDON, is an advanced software developed by a team of computer scientists led by Tassos Fragos from UNIGE and Vicky Kalogera from Northwestern University.
It is an open-source software developed to simulate binary star populations and study the formation and evolution of binary black hole systems.
POSYDON uses detailed simulations of single and binary stars to understand how binary systems evolve. It focuses on simulating the birth and evolution of binary-star systems to replicate the properties of merging binary black holes, which are challenging to observe directly.
Traditional simulations to study the evolution of millions of binary star systems require simplifications and predictions, which leads to less accurate predictions. POSYDON overcomes this by employing a pre-computed library of detailed simulations covering a range of initial conditions for binary systems.
"POSYDON can utilize this extensive dataset along with machine learning methods to predict the complete evolution of binary systems in less than a second. This speed is comparable to that of previous-generation rapid population synthesis codes, but with improved accuracy," said Jeffrey Andrews, assistant professor in the Department of Physics at the University of Florida, in a press release.
Exploring black holes with a new model
The team is the first to use POSYDON to study merging binary black hole systems.
It sheds new light on the mechanics of merging black holes in galaxies like our own. The research team is now working on a new version of POSYDON that will feature a more extensive library of detailed star and binary simulations that will be capable of modeling binaries in a wide variety of galaxies.
"Models prior to POSYDON predicted a negligible formation rate of merging binary black holes in galaxies similar to the Milky Way, and they particularly did not anticipate the existence of merging black holes as massive as 30 times the mass of our sun. POSYDON has demonstrated that such massive black holes might exist in Milky Way-like galaxies," explained Kalogera, who is also a co-author of the study.
POSYDON improves upon previous models by addressing issues with overestimations concerning the expansion of giant stars, which affects their mass loss and interactions in binary systems. By incorporating detailed simulations of stellar structure and binary interactions, POSYDON achieves better predictions of merging black hole properties like their masses and spins.
The future of black hole research looks promising, thanks to POSYDON!
The findings of the study were published in Nature Astronomy and can be found here.
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