Deep searches for broadband extended gravitational-wave emission bursts by heterogeneous computing

TL;DR

This paper introduces a GPU-accelerated heterogeneous algorithm for detecting broadband extended gravitational-wave emissions from astrophysical events, enabling efficient, near-real-time analysis of large data sets from LIGO.

Contribution

The paper presents a novel GPU-based matched filtering algorithm optimized for long-duration gravitational-wave signals, achieving high efficiency and real-time processing capabilities.

Findings

Attains about 65% efficiency normalized to FFT

Processes over one million correlations per second

Demonstrates successful detection of hardware LIGO injections

Abstract

We present a heterogeneous search algorithm for broadband extended gravitational-wave emission (BEGE), expected from gamma-ray bursts and energetic core-collapse supernovae. It searches the $(f,\dot{f})$-plane for long duration bursts by inner engines slowly exhausting their energy reservoir by matched filtering on a {\em Graphics Processor Unit} (GPU) over a template bank of millions of one-second duration chirps. Parseval's Theorem is used to predict the standard deviation $\sigma$ of filter output, taking advantage of near-Gaussian noise in LIGO S6 data over 350-2000 Hz. Tails exceeding a mulitple of $\sigma$ are communicated back to a {\em Central Processing Unit} (CPU). This algorithm attains about 65\% efficiency overall, normalized to the Fast Fourier Transform (FFT). At about one million correlations per second over data segments of 16 s duration $(N=2^{16}$ samples), better than real-time analysis is achieved on a cluster of about a dozen GPUs. We demonstrate its application to the capture of high frequency hardware LIGO injections. This algorithm serves as a starting point for deep all-sky searches in both archive data and real-time analysis in current observational runs.