GPU-accelerated massive black hole binary parameter estimation with LISA

摘要: The Laser Interferometer Space Antenna (LISA) is slated for launch in the early 2030s. A main target of mission massive black hole binaries that have an expected detection rate $\ensuremath{\sim}20\text{ }\text{ }{\mathrm{yr}}^{\ensuremath{-}1}$. We present a parameter estimation analysis variety binaries. This performed with graphics processing unit (GPU) implementation comprising phenomhm waveform higher-order harmonic modes and aligned spins; fast frequency-domain LISA detector response function; GPU-native likelihood computation. computational performance achieved GPU shown to be 500 times greater than similar CPU implementation, which allows us analyze full noise-infused injections at realistic Fourier bin width tractable efficient amount time. With these computations, we study effect adding spins by testing different configurations injection, as well varied fixed during sampling. Within tests, examine three varying mass ratios, redshifts, sky locations, detector-frame total masses ranging over 3 orders magnitude. discuss correlations between ratios; unique spin posteriors larger binaries; constraints on parameters when fixing sampling, allowing compare previous analyses did not include spins.