A fully-automated end-to-end pipeline for massive black hole binary signal extraction from LISA data

TL;DR

This paper introduces a fully-automated, GPU-accelerated pipeline for extracting massive black hole binary signals from LISA data, demonstrating rapid and accurate parameter estimation in simulated datasets.

Contribution

The authors develop a novel end-to-end pipeline utilizing heterodyning and GPU acceleration for efficient black hole binary signal extraction from LISA data.

Findings

Pipeline processes data in tens of minutes.

Accurate posterior distributions obtained for noiseless and noisy data.

Higher harmonics included for more realistic parameter estimation.

Abstract

The LISA Data Challenges Working Group within the LISA Consortium has started publishing datasets to benchmark, compare, and build LISA data analysis infrastructure as the Consortium prepares for the launch of the mission. We present our solution to the dataset from LISA Data Challenge (LDC) 1A containing a single massive black hole binary signal. This solution is built from a fully-automated and GPU-accelerated pipeline consisting of three segments: a brute-force initial search; a refining search that uses the efficient Likelihood computation technique of Heterodyning (also called Relative Binning) to locate the maximum Likelihood point; and a parameter estimation portion that also takes advantage of the speed of the Heterodyning method. This pipeline takes tens of minutes to evolve from randomized initial parameters throughout the prior volume to a converged final posterior distribution. Final posteriors are shown for both datasets from LDC 1A: one noiseless data stream and one containing additive noise. A posterior distribution including higher harmonics is also shown for a self-injected waveform with the same source parameters as is used in the original LDC 1A dataset. This higher-mode posterior is shown in order to provide a more realistic distribution on the parameters of the source.