Towards Realistic Detection Pipelines of Taiji: New Challenges in Data Analysis and High-Fidelity Simulations of Space-Based Gravitational Wave Antenna

Abstract

Taiji, a Chinese space-based gravitational wave (GW) detection project, aims to explore the millihertz GW universe with unprecedented sensitivity. By observing astrophysical and cosmological sources, including Galactic binaries, massive black hole binaries, extreme mass-ratio inspirals, and stochastic gravitational wave backgrounds, etc., Taiji is expected to deliver transformative insights into astrophysics, cosmology, and fundamental physics. However, Taiji's data analysis faces unique challenges compared to ground-based detectors like LIGO-Virgo-KAGRA, such as the overlap of numerous signals, extended data durations, more rigorous accuracy requirements for the waveform templates, incompletely characterized noise spectra, non-stationary noises, and various data anomalies. Taking Taiji as a representative example, this paper reviews the data characteristics and data analysis challenges of space-based GW detection, and introduces the second round of Taiji Data Challenge, a collection of simulation datasets designed as a shared platform for resolving these critical issues. This platform distinguishes itself from previous works by the systematic integration of orbital dynamics based on a full drag-free and attitude control simulation, extended noise sources, more complicated and overlapping GW signals, second-generation time-delay interferometry, and the coupling effect of time-varying arm-lengths, etc. Concurrently released is the open-source toolkit Triangle, which offers the capabilities for customized simulation of signals, noises, and other instrumental effects. By taking a step further towards realistic detection, Taiji Data Challenge II and Triangle altogether serve as a new testbed, supporting the development of Taiji's global analysis and end-to-end pipelines, and ultimately bridging the gaps between observation and scientific objectives.

Figures (13)

• Figure 1: A schematic diagram of Taiji constellation. Each SC is equipped with two movable optical sub-assemblies (MOSAs), which is a structure connecting a telescope, a gravitational reference sensor (GRS) that hosts a test-mass (TM), an optical bench carrying the laser sources, phasemeters, and other optical components needed for interferometric measurements, etc. Each MOSA is labeled by $ij$ ($ij \in \{12, 23, 31, 21, 32, 13\}$), with $i$ being the index of SC carrying this MOSA, and $j$ the index of distant SC that transmits lasers with this MOSA. The indexing of on-board interferometric measurements $\{ s_{ij}, \tau_{ij}, \varepsilon_{ij} \}$ are consistent with the MOSAs. For laser link $L_{ij}$, $i$ and $j$ denote the SC that receives and emits laser, respectively.
• Figure 2: The scientific data flow of Taiji.
• Figure 3: Detectability of the higher harmonics, showcased using the parameters of the 3rd MBHB from dataset 1.1. The green solid curve represents the waveform (in terms of TDI-$X_2$ response) containing only the dominant (2,2) mode, while the orange dotted curve incorporates 6 harmonic modes: (2,1), (3,3), (3,2), (4,4), and (4,3). Systematic deviations caused by neglecting higher modes are quantified by the residual (yellow line), compared against the instrument noise floor (gray curve) defining the detection threshold. This mismatch necessitate the inclusion of higher modes for unbiased parameter estimation in the high-SNR regime.
• Figure 4: Systematic errors induced by inaccurate orbit model. Using the same source parameters as FIG. \ref{['fig:higher_mode_mbhb']} as an example, the data of TDI-$X_2$ channel is simulated in the time domain, with GW response calculated based on a numerical orbit. Notice that we have excluded instrumental noises to isolate systematic biases from statistical uncertainties. When estimating the MBHB's source parameters, two response templates are employed: (1) faithfully adopting the same numerical orbit, and (2) using an analytical equal-armlength approximation closest to the numerical orbit. The left panel illustrates the discrepancy between these two templates, while comparisons on the estimates for intrinsic and extrinsic (3D localization) parameters are presented in the middle and right panels, respectively. Evidently, for MBHBs with SNRs exceeding $10^3$, the incorporation of realistic orbital information is critical to avoid significant systematic biases.
• Figure 5: The TM ACC noises (left panel) and SC jitter noises (right panel) derived from the numerical simulation for the DFACS. Each noise component meets Taiji's design sensitivity requirements (black dashed curves), yet none of them are fully aligned with the design curves.