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

Duncan Meacher, Kipp Cannon, Chad Hanna, Tania Regimbau, B. S. Sathyaprakash

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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 1 Hz. 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 analyze 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 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.

Original languageEnglish (US)
Article number024018
JournalPhysical Review D
Volume93
Issue number2
DOIs
StatePublished - Jan 11 2016

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy (miscellaneous)

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