Detecting Long-Duration Narrow-Band Gravitational Wave Transients Associated with Soft Gamma Repeater Quasi-Periodic Oscillations

TL;DR

This paper presents a novel gravitational wave data analysis method tailored for detecting long-duration, narrow-band transients associated with soft gamma repeater QPOs, capable of handling non-Gaussian noise and changing detector orientations.

Contribution

The paper introduces a new multi-trigger data analysis approach that does not assume Gaussian noise, demonstrating its effectiveness on simulated and real LIGO data for soft gamma repeater signals.

Findings

Effective glitch rejection and outlier suppression in LIGO S5 data

Method successfully identifies simulated QPO signals in mock data

Potential for extension to multi-detector networks and real astrophysical events

Abstract

We have performed an in-depth concept study of a gravitational wave data analysis method which targets repeated long quasi-monochromatic transients (triggers) from cosmic sources. The algorithm concept can be applied to multi-trigger data sets in which the detector-source orientation and the statistical properties of the data stream change with time, and does not require the assumption that the data is Gaussian. Reconstructing or limiting the energetics of potential gravitational wave emissions associated with quasi-periodic oscillations (QPOs) observed in the X-ray lightcurve tails of soft gamma repeater flares might be an interesting endeavour of the future. Therefore we chose this in a simplified form to illustrate the flow, capabilities, and performance of the method. We investigate performance aspects of a multi-trigger based data analysis approach by using O(100 s) long stretches of mock data in coincidence with the times of observed QPOs, and by using the known sky location of the source. We analytically derive the PDF of the background distribution and compare to the results obtained by applying the concept to simulated Gaussian noise, as well as off-source playground data collected by the 4-km Hanford detector (H1) during LIGO's fifth science run (S5). We show that the transient glitch rejection and adaptive differential energy comparison methods we apply succeed in rejecting outliers in the S5 background data. Finally, we discuss how to extend the method to a network containing multiple detectors, and as an example, tune the method to maximize sensitivity to SGR 1806-20 flare times.