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The Einstein Telescope is a conceived third generation gravitational-wavedetector that is envisioned to be an order of magnitude more sensitive thanadvanced LIGO, Virgo and Kagra, which would be able to detectgravitational-wave signals from the coalescence of compact objects withwaveforms starting as low as 1Hz. With this level of sensitivity, we expect todetect sources at cosmological distances. In this paper we introduce animproved method for the generation of mock data and analyse it with a new lowlatency compact binary search pipeline called gstlal. We present the resultsfrom this analysis with a focus on low frequency analysis of binary neutronstars. Despite compact binary coalescence signals lasting hours in the EinsteinTelescope sensitivity band when starting at 5 Hz, we show that we are able todiscern various overlapping signals from one another. We also determine thedetection efficiency for each of the analysis runs conducted and and show aproof of concept method for estimating the number signals as a function ofredshift. Finally, we show that our ability to recover the signal parametershas improved by an order of magnitude when compared to the results of the firstmock data and science challenge. For binary neutron stars we are able torecover the total mass and chirp mass to within 0.5% and 0.05%, respectively.

Second Einstein Telescope Mock Data and Science Challenge: Low Frequency Binary Neutron Star Data Analysis