BlackHoles@Home: First Gravitational Waveform Catalog

Each observation of a gravitational wave (GW) is compared against millions of theoretical predictions to perform parameter estimation and extract important science. Our most reliable GW predictions build upon catalogs of numerical relativity (NR) compact binary coalescence simulations, which to date has always required a computing cluster. Such large computational expense has limited the catalog size to only about 3,000 in 15 years. Given the vast parameter space of even the simplest (but most commonly observed) GW source---binary black holes (BBHs)---this small GW collection threatens potential science gains from future GW observations.

BlackHoles@Home is a proposed BOINC project that leverages new NR techniques to fit BBH simulations on a consumer-grade desktop computer, enabling GW follow-ups and catalogs with unprecedented throughput using volunteer computers. We recently showed that new numerical gridding algorithms enable BlackHoles@Home to model BBH inspirals, mergers, and ringdowns on consumer-grade desktop computers in about 1/100 the amount of memory of the most popular gridding approach in NR: adaptive-mesh refinement. We further find higher-order GW modes exhibit less noise than any other NR code, and that numerical errors converge cleanly to zero. We present the first-ever BlackHoles@Home waveform catalog, as well as recent improvements to the source code, which focus on robustness and scalability.