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Computer Simulations Point To the Source of Gravitational Waves (theverge.com)

An anonymous reader writes from a report via The Verge: On February 11th, scientists at the LIGO observatory made history when they announced the detection of the first gravitational waves. A new study says the gravitational waves likely came from two massive suns that formed about 12 billion years ago, or two billion years after the Big Bang. The researcher's calculations have been published today in the journal Nature, and were determined by running a complex simulation called the Synthetic Universe: a computer model that simulates how the Universe may have evolved since the start of the Big Bang. The simulation even includes a synthetic LIGO detector to determine the types of objects that the observatory would detect over time. The Synthetic Universe can also make predictions as it includes a mock-LIGO to chronologically sync when we detected the waves. If the model is correct, we should see LIGO pick up to 60 detections when it begins its next observation run this fall. It could hear up to 1,000 detections annually at its peak sensitivity. The lead study author Chris Belczynski speculates specifically the size of black hole mergers that the LIGO should be able to detect from gravitational waves, a combined mass between 20 and 80 times the mass of our sun, indicating that they're likely from soon after the Big Bang when stars had lower metal content and formed proportionately larger black holes. His model suggests that the ones that collided to make these gravitational waves were stars that formed 12 billion years ago, became black holes 5 million years later, and then merged 10.3 billion years after that.

5 of 126 comments (clear)

  1. Re:Practical value? by Rei · · Score: 5, Informative

    The practical value is that we finally have a way to probe one of the biggest parts of physics that we don't actually understand, gravity. And it has direct implications for all of the other aspects that we don't understand or have major questions about, such as inflation, dark energy, the unification of relativity and quantum physics, etc. The field has massive potential to further our understanding of physics and the universe that we live in.

    And LIGO is only the start. When something like eLISA comes online it'd be like going from the blurry images of Galileo's telescope to an actual astronomical observatory.

    --
    Did he just go crazy and fall asleep?
  2. Does the simulation ... by Bugdanoff · · Score: 5, Funny

    > "The simulation even includes a synthetic LIGO detector to determine the types of objects that the observatory would detect over time."
    Does the simulation also include a synthetic simulation in order to determine what it would find out by simulating the universe ?

  3. Re:Practical value? by wonkey_monkey · · Score: 5, Insightful

    What is the practical value of this?

    What's the practical value of you?

    Furthermore, there's already abundant evidence supporting relativity (which does have practical uses)

    It does now. What practical uses did it have when (or before) it was discovered?

    --
    systemd is Roko's Basilisk.
  4. Re:Wrong! They were made by Jesus by Rei · · Score: 5, Funny

    They don't want to give Jesus any credit, ever.

    That's only because he doesn't show any fiscal responsibility. He's always giving his money away to the poor and lepers, getting in trouble with the law, etc. If we moneychangers were to extend him a line of credit, why should we expect to ever get paid back?

    Not that I'd want to tell him that to his face - the last time he stopped by he trashed the place and started attacking us with a whip. That guy is mental.

    --
    Did he just go crazy and fall asleep?
  5. Re:Simulations - Program them to agree with you by Anonymous Coward · · Score: 5, Informative

    No, what you do is see something, try to figure out how it might work and model it. At this point you probably have a model that "works" because it fits your observation - not very useful. But then you use it to predict what else you might see. If the prediction matches the next observation, it strengthens the possibility that the model might actually describe something fundamental, more and more so as it gives consistent results over many observations. If the prediction doesn't match, you figure there's something wrong with the model and start again, or you refine the model, and so on and so on.