Fast non-Markovian sampler for estimating gravitational-wave posteriors

This article introduces VARAHA, an open-source, fast, non-Markovian sampler for estimating gravitational-wave posteriors. VARAHA differs from existing nested sampling algorithms by gradually discarding regions of low likelihood, rather than gradually sampling regions of high likelihood. This alternative mindset enables VARAHA to freely draw samples from anywhere within the high-likelihood region of the parameter space, allowing for analyses to complete in significantly fewer cycles. This means that VARAHA can significantly reduce both the wall and CPU time of all analyses. VARAHA offers many benefits, particularly for gravitational-wave astronomy where Bayesian inference can take many days, if not weeks, to complete. For instance, VARAHA can be used to estimate accurate sky locations, astrophysical probabilities and source classifications within minutes, which is particularly useful for multimessenger follow-up of binary neutron star observations; VARAHA localizes GW170817 & SIM;30 times faster than LALInference. Although only aligned-spin, dominant multipole waveform models can be used for gravitational-wave analyses, it has the potential to include additional physics. We envision VARAHA being used for gravitational-wave studies, particularly estimating parameters using expensive waveform models, analyzing subthreshold gravitational-wave candidates, generating simulated data for population studies, and rapid posterior estimation for binary neutron star mergers.

Automated summary

VARAHA is an open-source, fast, non-Markovian sampler used for estimating gravitational-wave posteriors. It differs from existing sampling algorithms by discarding low-likelihood regions instead of sampling high-likelihood regions, allowing for significantly faster analyses. VARAHA offers many benefits, such as accurate sky localization and rapid posterior estimation, making it useful for gravitational-wave studies, particularly for binary neutron star observations.