Searching for stellar-origin binary black holes in LISA Data Challenge 1b: Yorsh

TL;DR

This paper presents a hierarchical, GPU-accelerated search method for detecting stellar-origin binary black holes in simulated LISA data, successfully identifying all injected sources with high confidence.

Contribution

It introduces a novel hierarchical search algorithm using semi-coherent matching and particle swarm optimization, optimized with GPU acceleration, for LISA data analysis.

Findings

Successfully detected all five injected sources with SNR ≥ 12

Demonstrated effective GPU-accelerated hierarchical search pipeline

Performed rapid parameter estimation for identified sources

Abstract

This paper reports the first search for stellar-origin binary black holes within the LISA Data Challenges (LDC). The search algorithm and the \Yorsh{} LDC datasets, both previously described elsewhere, are only summarized briefly; the primary focus here is to present the results of applying the search to the challenge of data. The search employs a hierarchical approach, leveraging semi-coherent matching of template waveforms to the data using a variable number of segments, combined with a particle swarm algorithm for parameter space exploration. The computational pipeline is accelerated using graphical processing unit (GPU) hardware. The results of two searches using different models of the LISA response are presented. The most effective search finds all five sources in the data challenge with injected signal-to-noise ratios $\gtrsim 12$. Rapid parameter estimation is performed for these sources.