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The Laser Interferometer Space Antenna (LISA) is slated for launch in theearly 2030s. A main target of the mission is massive black hole binaries thathave an expected detection rate of $\sim20$ yr$^{-1}$. We present a parameterestimation analysis for a variety of massive black hole binaries. This analysisis performed with a graphics processing unit (GPU) implementation comprisingthe phenomhm waveform with higher-order harmonic modes and aligned spins; afast frequency-domain LISA detector response function; and a GPU-nativelikelihood computation. The computational performance achieved with the GPU isshown to be 500 times greater than with a similar CPU implementation, whichallows us to analyze full noise-infused injections at a realistic Fourier binwidth for the LISA mission in a tractable and efficient amount of time. Withthese fast likelihood computations, we study the effect of adding aligned spinsto an analysis with higher-order modes by testing different configurations ofspins in the injection, as well as the effect of varied and fixed spins duringsampling. Within these tests, we examine three different binaries with varyingmass ratios, redshifts, sky locations, and detector-frame total masses rangingover three orders of magnitude. We discuss varied correlations between thetotal masses and mass ratios; unique spin posteriors for the larger massbinaries; and the constraints on parameters when fixing spins during sampling,allowing us to compare to previous analyses that did not include aligned spins.

GPU-accelerated massive black hole binary parameter estimation with LISA