A mock data challenge for next-generation detectors

Abstract

The Einstein Telescope (ET), a planned third-generation gravitational-wave (GW) observatory, will offer significantly improved sensitivity, introducing new challenges for data analysis and computing. To prepare for these demands, the ET community has initiated a series of Mock Data Challenges (MDCs) aimed at developing and testing analysis pipelines under realistic conditions. This paper presents the first ET MDC, providing an overview of the simulated dataset and the properties of the injected GW signals, with a focus on populations of compact binary coalescences and Gaussian noise. A tutorial is also included to guide users in accessing the data and performing basic analyses. This initial challenge establishes a baseline for future MDCs and supports collaborative efforts toward the successful scientific operation of the ET.

Figures (16)

• Figure 1: Flowchart summarizing the Monte Carlo procedure used to generate the population of compact binary coalescences and simulate the corresponding ET detector time series.
• Figure 4: Time series of the first 2048-second data segment sampled at 8192 Hz. The blue curve shows a realization of detector noise, while the black curve represents the GW signal from CBCs, as also shown in Fig. \ref{['fig-GWsignal']}.
• Figure 5: Comparison between the predicted noise power spectral density (dashed black line) and the PSD estimated from the first 2048-second segment of simulated ET data for E1, E2, E3, and the null stream E0. The agreement confirms the fidelity of the noise simulation process and indicates that the null stream cancels the GW signal while retaining the instrumental noise characteristics.
• Figure 6: Time series of the GW signal (in blue) within the first 2048-second data segment sampled at 8192 Hz. The null stream is indicated in red.
• Figure 13: Optimal signal-to-noise ratio ($\rho_{opt}$) as a function of the observed total mass $M_{z,tot}$.