Application of a Zero-latency Whitening Filter to Compact Binary Coalescence Gravitational-wave Searches

Joint electromagnetic and gravitational-wave (GW) observation is a major goal of both the GW astronomy and electromagnetic astronomy communities for the coming decade. One way to accomplish this goal is to direct follow-up of GW candidates. Prompt electromagnetic emission may fade quickly, therefore it is desirable to have GW detection happen as quickly as possible. A leading source of latency in GW detection is the whitening of the data. We examine the performance of a zero-latency whitening filter in a detection pipeline for compact binary coalescence (CBC) GW signals. We find that the filter reproduces signal-to-noise ratio (SNR) sufficiently consistent with the results of the original high-latency and phase-preserving filter for both noise and artificial GW signals (called "injections"). Additionally, we demonstrate that these two whitening filters show excellent agreement in $\chi^2$ value, a discriminator for GW signals.Comment: 8 pages, 12 figure

Second Einstein Telescope mock data and science challenge: Low frequency binary neutron star data analysis

The Einstein Telescope is a conceived third generation gravitational-wave detector that is envisioned to be an order of magnitude more sensitive than advanced LIGO, Virgo and Kagra, which would be able to detect gravitational-wave signals from the coalescence of compact objects with waveforms starting as low as 1Hz. With this level of sensitivity, we expect to detect sources at cosmological distances. In this paper we introduce an improved method for the generation of mock data and analyse it with a new low latency compact binary search pipeline called gstlal. We present the results from this analysis with a focus on low frequency analysis of binary neutron stars. Despite compact binary coalescence signals lasting hours in the Einstein Telescope sensitivity band when starting at 5 Hz, we show that we are able to discern various overlapping signals from one another. We also determine the detection efficiency for each of the analysis runs conducted and and show a proof of concept method for estimating the number signals as a function of redshift. Finally, we show that our ability to recover the signal parameters has improved by an order of magnitude when compared to the results of the first mock data and science challenge. For binary neutron stars we are able to recover the total mass and chirp mass to within 0.5% and 0.05%, respectively

A Mock Data and Science Challenge for Detecting an Astrophysical Stochastic Gravitational-Wave Background with Advanced LIGO and Advanced Virgo

The purpose of this mock data and science challenge is to prepare the data analysis and science interpretation for the second generation of gravitational-wave experiments Advanced LIGO-Virgo in the search for a stochastic gravitational-wave background signal of astrophysical origin. Here we present a series of signal and data challenges, with increasing complexity, whose aim is to test the ability of current data analysis pipelines at detecting an astrophysically produced gravitational-wave background, test parameter estimation methods and interpret the results. We introduce the production of these mock data sets that includes a realistic observing scenario data set where we account for different sensitivities of the advanced detectors as they are continuously upgraded toward their design sensitivity. After analysing these with the standard isotropic cross-correlation pipeline we find that we are able to recover the injected gravitational-wave background energy density to within $2\sigma$ for all of the data sets and present the results from the parameter estimation. The results from this mock data and science challenge show that advanced LIGO and Virgo will be ready and able to make a detection of an astrophysical gravitational-wave background within a few years of operations of the advanced detectors, given a high enough rate of compact binary coalescing events

A Mock Data Challenge for the Einstein Gravitational-Wave Telescope

Einstein Telescope (ET) is conceived to be a third generation gravitational-wave observatory. Its amplitude sensitivity would be a factor ten better than advanced LIGO and Virgo and it could also extend the low-frequency sensitivity down to 1--3 Hz, compared to the 10--20 Hz of advanced detectors. Such an observatory will have the potential to observe a variety of different GW sources, including compact binary systems at cosmological distances. ET's expected reach for binary neutron star (BNS) coalescences is out to redshift $z\simeq 2$ and the rate of detectable BNS coalescences could be as high as one every few tens or hundreds of seconds, each lasting up to several days. %in the sensitive frequency band of ET. With such a signal-rich environment, a key question in data analysis is whether overlapping signals can be discriminated. In this paper we simulate the GW signals from a cosmological population of BNS and ask the following questions: Does this population create a confusion background that limits ET's ability to detect foreground sources? How efficient are current algorithms in discriminating overlapping BNS signals? Is it possible to discern the presence of a population of signals in the data by cross-correlating data from different detectors in the ET observatory? We find that algorithms currently used to analyze LIGO and Virgo data are already powerful enough to detect the sources expected in ET, but new algorithms are required to fully exploit ET data.Comment: accepted for publication in Physical Review D -- 18 pages, 8 figure

The GstLAL Search Analysis Methods for Compact Binary Mergers in Advanced LIGO's Second and Advanced Virgo's First Observing Runs

After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period in the GstLAL-based inspiral pipeline, which is used to detect gravitational waves from the coalescence of compact binaries both in low latency and an offline configuration. These updates include deployment of a zero-latency whitening filter to reduce the over-all latency of the pipeline by up to 32 seconds, incorporation of the Virgo data stream in the analysis, introduction of a single-detector search to analyze data from the periods when only one of the detectors is running, addition of new parameters to the likelihood ratio ranking statistic, increase in the parameter space of the search, and introduction of a template mass-dependent glitch-excision thresholding method.Comment: 12 pages, 7 figures, to be submitted to Phys. Rev. D, comments welcom