Speaker
Description
The Einstein Telescope (ET), a third-generation gravitational-wave (GW) observatory, will extend the sensitivity and bandwidth of current detectors, enabling observations from ~1 Hz and vastly increasing the detection rate of compact binary coalescences (CBCs). This leap in capability introduces new data analysis challenges, including increased rates of overlapping signals that can generate confusion noise and limit the effectiveness of standard matched-filter techniques. ET's triangular configuration allows the construction of a null stream—a linear combination of detector outputs in which GW signals cancel—offering a powerful tool for background estimation and glitch mitigation. In this work, we present a proof-of-concept study using simulated ET Mock Data Challenge (MDC) data to explore the role of the null stream in enhancing search sensitivity. We implement a simplified PyCBC-based search using a non-spinning BBH template bank and test the utility of the null stream for improving background estimation in the presence of overlapping signals in Gaussian noise. These initial results highlight the potential of null stream methods in addressing key analysis challenges anticipated in ET-era searches.
