GPU-accelerated massive black hole binary parameter estimation with LISA

Abstract

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

27 pages, 13 figures

References in corpus (23)

• The NumPy array: a structure for efficient numerical computation
• Formation of Supermassive Black Holes by Direct Collapse in Pregalactic Halos
• Gravitational-wave sensitivity curves
• Use and Abuse of the Fisher Information Matrix in the Assessment of Gravitational-Wave Parameter-Estimation Prospects
• Science with the space-based interferometer LISA. IV: Probing inflation with gravitational waves
• Formation of the first nuclear clusters and massive black holes at high redshift
• Accretion onto the First Stellar Mass Black Holes
• BayesLine: Bayesian Inference for Spectral Estimation of Gravitational Wave Detector Noise
• Can Supermassive Black Holes Form in Metal-Enriched High-Redshift Protogalaxies ?
• LISA detections of massive black hole inspirals: parameter extraction errors due to inaccurate template waveforms
• The Mock LISA Data Challenges: from Challenge 3 to Challenge 4
• LISA observations of supermassive black holes: parameter estimation using full post-Newtonian inspiral waveforms
• The Effect of Higher Harmonic Corrections on the Detection of massive black hole binaries with LISA
• MCMC Exploration of Supermassive Black Hole Binary Inspirals
• Catching Super Massive Black Hole Binaries Without a Net
• Supermassive Black Hole Binaries: The Search Continues
• A Markov Chain Monte Carlo approach to the study of massive black hole binary systems with LISA
• A Three-Stage Search for Supermassive Black Hole Binaries in LISA Data
• Fisher vs. Bayes : A comparison of parameter estimation techniques for massive black hole binaries to high redshifts with eLISA
• Parameter estimation of coalescing supermassive black hole binaries with LISA
• Massive Black Hole Science with eLISA
• Inference on inspiral signals using LISA MLDC data
• Resolving Super Massive Black Holes with LISA

Cited by in corpus (1)

• Computational Techniques for Parameter Estimation of Gravitational Wave Signals