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 bin...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(available at https://github.com/TriangleDataCenter),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.展开更多
基金supported by the National Key Research and Development Program of China (Grant Nos.2024YFC2207300,2021YFC2201903,2021YFC2201901,2020YFC2200100)。
文摘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(available at https://github.com/TriangleDataCenter),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.
