  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.
