BlackHoles@Home: Difference between revisions
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[[File:{{#setmainimage:bh.png}}|alt=BlackHoles@Home logo image|center|frameless]] | [[File:{{#setmainimage:bh.png}}|alt=BlackHoles@Home logo image|center|frameless]] | ||
[https://blackholesathome.net/ '''''BlackHoles@Home'''''] is a '''''[[wikipedia:Volunteer computing|volunteer distributed computing]]''''' project that needs your help to | [https://blackholesathome.net/ '''''BlackHoles@Home'''''] is a '''''[[wikipedia:Volunteer computing|volunteer distributed computing]]''''' project that needs your help to run Black hole collision simulations to maximize the science gained from gravitational wave observations. | ||
== Why BlackHoles@Home? == | == Why BlackHoles@Home? == | ||
When a gravitational wave is observed, answering the question "What exactly produced this?" is crucial to advancing science. Inferring physical properties of even the simplest observed gravitational wave source-black hole binaries-requires catalogs of numerical relativity gravitational waveforms spanning all seven dimensions of intrinsic parameter space (i.e., mass ratio, plus the three spin vector components of each black hole). Due to the requirement that virtually all numerical relativity simulations to date be run on supercomputers, all such catalogs combined sample this parameter space to only about 3 points per dimension. | |||
These tiny catalogs have been sufficient for noisy gravitational wave observations to date, as the noise acts to obscure the relatively small effects of misaligned spins, but they will not be good enough moving forward. | |||
== Goal == | == Goal == | ||
BlackHoles@Home aims to reduce the cost of numerical relativity black hole and neutron star binary simulations by ~100x, through adoption of numerical grids that fully exploit near-symmetries in these systems. | |||
With this cost savings, black hole binary merger simulations can be performed entirely on a consumer-grade desktop (or laptop) computer.[https://etienneresearch.com/] | |||
== Methods == | == Methods == | ||
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== Project team / Sponsors == | == Project team / Sponsors == | ||
Prof. Z. Etienne. Primarily funded by NSF grant PHY-1806596. It is also funded by NSF EPSCoR Grant 1458952 and NASA grants 80NSSC18K0538 and 80NSSC18K1488. | |||
Revision as of 12:49, 9 March 2024
[[File:{{#setmainimage:bh.png}}|alt=BlackHoles@Home logo image|center|frameless]]
BlackHoles@Home is a volunteer distributed computing project that needs your help to run Black hole collision simulations to maximize the science gained from gravitational wave observations.
Why BlackHoles@Home?
When a gravitational wave is observed, answering the question "What exactly produced this?" is crucial to advancing science. Inferring physical properties of even the simplest observed gravitational wave source-black hole binaries-requires catalogs of numerical relativity gravitational waveforms spanning all seven dimensions of intrinsic parameter space (i.e., mass ratio, plus the three spin vector components of each black hole). Due to the requirement that virtually all numerical relativity simulations to date be run on supercomputers, all such catalogs combined sample this parameter space to only about 3 points per dimension.
These tiny catalogs have been sufficient for noisy gravitational wave observations to date, as the noise acts to obscure the relatively small effects of misaligned spins, but they will not be good enough moving forward.
Goal
BlackHoles@Home aims to reduce the cost of numerical relativity black hole and neutron star binary simulations by ~100x, through adoption of numerical grids that fully exploit near-symmetries in these systems.
With this cost savings, black hole binary merger simulations can be performed entirely on a consumer-grade desktop (or laptop) computer.[1]
Methods
- always including "why BOINC"?
- (Optional) insert MediaWiki image or upload
example MediaWiki image - impactful final statement
Project team / Sponsors
Prof. Z. Etienne. Primarily funded by NSF grant PHY-1806596. It is also funded by NSF EPSCoR Grant 1458952 and NASA grants 80NSSC18K0538 and 80NSSC18K1488.