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

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.Comment: 11 pages, 8 figure

The Advanced LIGO timing system

Gravitational wave detection using a network of detectors relies upon the precise time stamping of gravitational wave signals. The relative arrival times between detectors are crucial, e.g. in recovering the source direction, an essential step in using gravitational waves for multi-messenger astronomy. Due to the large size of gravitational wave detectors, timing at different parts of a given detector also needs to be highly synchronized. In general, the requirement toward the precision of timing is determined such that, upon detection, the deduced (astro-) physical results should not be limited by the precision of timing. The Advanced LIGO optical timing distribution system is designed to provide UTC-synchronized timing information for the Advanced LIGO detectors that satisfies the above criterium. The Advanced LIGO timing system has modular structure, enabling quick and easy adaptation to the detector frame as well as possible changes or additions of components. It also includes a self-diagnostics system that enables the remote monitoring of the status of timing. After the description of the Advanced LIGO timing system, several tests are presented that demonstrate its precision and robustness

Constraining Black Hole Populations in Globular Clusters using Microlensing: Application to Omega Centauri

We estimate the rate of gravitational microlensing events of cluster stars due to black holes (BHs) in the globular cluster NGC 5139 ($\omega Cen$). Theory and observations both indicate that $\omega Cen$ may contain thousands of BHs, but their mass spectrum and exact distribution are not well constrained. In this Letter we show that one may observe microlensing events on a timescale of years in $\omega Cen$, and such an event sample can be used to infer the BH distribution. Direct detection of BHs will, in the near future, play a major role in distinguishing binary BH merger channels. Here we explore how gravitational microlensing can be used to put constraints on BH populations in globular clusters.Comment: 6 pages, 2 figures, published in ApJ

Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks

Gravitational wave astronomy is a vibrant field that leverages both classic and modern data processing techniques for the understanding of the universe. Various approaches have been proposed for improving the efficiency of the detection scheme, with hierarchical matched filtering being an important strategy. Meanwhile, deep learning methods have recently demonstrated both consistency with matched filtering methods and remarkable statistical performance. In this work, we propose Hierarchical Detection Network (HDN), a novel approach to efficient detection that combines ideas from hierarchical matching and deep learning. The network is trained using a novel loss function, which encodes simultaneously the goals of statistical accuracy and efficiency. We discuss the source of complexity reduction of the proposed model, and describe a general recipe for initialization with each layer specializing in different regions. We demonstrate the performance of HDN with experiments using open LIGO data and synthetic injections, and observe with two-layer models a $79\%$ efficiency gain compared with matched filtering at an equal error rate of $0.2\%$. Furthermore, we show how training a three-layer HDN initialized using two-layer model can further boost both accuracy and efficiency, highlighting the power of multiple simple layers in efficient detection