Weakly modeled search for compact binary coalescences in the Einstein Telescope

Einstein Telescope (ET) is a third-generation gravitational-wave (GW) detector project which aims to reach a sensitivity $\ensuremath{\sim}10$ times larger than current GW detectors such as Advanced LIGO and Advanced Virgo. The high rate of GW signals expected in the data will pose several data analysis challenges, such as the ability to disentangle overlapping signals, or the computational resources required to treat all the candidate events. We explore the behavior and the performances of pystampas, a data analysis pipeline designed to search for unmodeled GW signals with duration $\ensuremath{\sim}1--1000\text{ }\text{ }\mathrm{s}$ on a mock dataset that consists of 1 month of data following ET design sensitivity on top of which is added a realistic distribution of compact binary coalescence (CBC) signals. Unmodeled searches are intrinsically less sensitive to CBC signals than template-based searches, but are computationally cheaper and more robust to uncertainties in the waveforms. This search recovers 38% of the total number of injected binary black hole (BBH) signals, including 89% of the systems with a total mass above $100{M}_{\ensuremath{\bigodot}}$, as well as the majority of binary neutron star (BNS) signals closer than 850 Mpc ($z=0.17$). It is also able to estimate the chirp mass of the recovered BNS with an average precision of 1.3%. Therefore, we show that this unmodeled search is able to detect a substantial amount of CBC events at a relatively low computational cost, which makes it interesting for low-latency analyses and independent validation of detections made by matched filtering pipelines. We also find that the presence of many CBC signals only marginally impacts the sensitivity of the search to other kinds of unmodeled long-duration transient signals, by $\ensuremath{\sim}3%$ in average.