The Gravitational Wave (GW) Spectrum and Space-Based Detection

Proposed orbit configurations of Taiji, TianQin, and LISA

Adapted from Gong et al., Nature Astronomy (2021).

Spectrum of Gravitational Wave

A brief history: from early studies to Taiji-1 to

Wen-Rui Hu , Yue-Liang Wu, Natl. Sci. Rev. Vol 4 (2017)

Yue-Liang Wu et al., Commun. Phys. 4, 34 (2021)

2008

— Early conceptual studies initialized by CAS on space-based gravitational-wave detection.

2010

— Early national proposal for space-based GW observation.

— Officially named Taiji, and publicly introduced to the international community.

2016

— A major milestone: the launch of Taiji-1 satellite as an in-orbit demonstrator for Taiji's technologies.

2018-2019

— Overall mission design and development roadmap were published.

2021

— Current focus: engineering preparation, system-level verification, data challenges, and data pipeline development.

Current

Taiji has evolved from early conceptual studies to in-orbit technology verification and is now moving toward full-mission engineering and science-data readiness.

The millihertz window

Designed sensitivities and target signals of LISA, Taiji and TianQin

HW, Minghui Du et al., Sci Sin-Phys Mech Astron 54, 270403 (2024)

We are targeting a broad range of mHz sources: MBHB/IMBH mergers, Galactic compact binaries, EMRIs/IMRIs, stellar-mass BBHs, stochastic backgrounds, and unknown sources.

Galactic Binaries (GBs)

Massive Black Hole Binaries (MBHBs)

Extreme Mass-Ratio Inspirals (EMRIs)

stellar-mass Black Hole Binaries (sBBHs)

Stochastic GW Backgrounds (SGWBs)

The millihertz window

Galactic Binaries (GBs)

Massive Black Hole Binaries (MBHBs)

Extreme Mass-Ratio Inspirals (EMRIs)

stellar-mass Black Hole Binaries (sBBHs)

Stochastic GW Backgrounds (SGWBs)

Designed sensitivities and target signals of LISA, Taiji and TianQin

HW, Minghui Du et al., Sci Sin-Phys Mech Astron 54, 270403 (2024)

We are targeting a broad range of mHz sources: MBHB/IMBH mergers, Galactic compact binaries, EMRIs/IMRIs, stellar-mass BBHs, stochastic backgrounds, and unknown sources.

To achieve this sensitivity, baseline mission requirements:

Three drag-free spacecrafts forming a near-equilateral triangular constellation
Arm length: \(3\times 10^6\) km
Heliocentric orbit, leading/trailing the Earth by ~\(20^\circ\)
Laser displacement sensitivity: \(8\times 10^{-12}\mathrm{m} /\sqrt{\mathrm{Hz}}\)
Test-mass acceleration noise: \(3 \times 10^{-15}\,\mathrm{m\,s^{-2}}/\sqrt{\mathrm{Hz}}\)

Taiji Full-Mission Concept

A heliocentric triangular constellation for mHz gravitational-wave astronomy

100-m underground interferometer

2008-2016

2018-2019

2020-202x

202x-203x

From Taiji-1 to full-mission preparation

Early studies and
mission formulation

Taiji-1 in-orbit
demonstration

Key technology
development

Full-mission
engineering
preparation

concept · proposal ·
public introduction

technology demo ·
launch · in-orbit
validation

drag-free · laser
interferometry ·
phasemeter ·
grav. reference sensor

integration · verification · data challenges

10-m ground interferometer

Outline of Taiji Data Processing Pipeline

The analysis starts from raw telemetry and ends at scientific interpretations

Developing data processing pipeline is also a key part of mission design and preparation.

Mock Data Challenge serves as benchmark for reflecting and addressing the potential problems.

Schematic for the data processing pipeline:

pre-processing stage

scientific-analysis stage

Taiji Data Challenge

Waveform:

A typical criterion for unbiased parameter estimation:

What are the challenges? These are what we found from the simulations:

Detector response:

The effect of realistic orbital motion (especially armlength variation ) in response:

1. Complexity in waveform and response modeling

subdominant features such as HMs, precession of MBHBs can no longer be ignored

bias in MBHB parameter estimation due to over-simplified detector orbit model

Taiji Data Challenge

A typical criterion for unbiased parameter estimation:

1. Complexity in waveform and response modeling

The effect of realistic orbital motion (especially armlength variation ) in response:

sensitivity averaged over a year, with all the covariance among TDI channels considered

What are the challenges? These are what we found from the simulations:

Taiji Data Challenge

colored curves: ACC noise from multi-physical field & DFACS simulation

black dashed curve: requirement of Taiji's target sensitivity

2. The unknown and complicated noise properties (non-stationarity, glitches and gaps)

What are the challenges? These are what we found from the simulations:

The non-stationarity of T channel's sensitivity introduced by arm variation. Also, It fails to serve as a null channel in the unequal-arm regime.

Taiji Data Challenge

3. The coupling between pre-processing and scientific-analysis stages

What are the challenges? These are what we found from the simulations:

PD4L channel is more robust due to less zero points in the noise spectrum than Michelson channel (much less divergence in the evaluation of likelihood)

Discovered by Gang Wang in 2502.03983, verified via TDC simulation.

Michelson-X

PD4L-1

as a crucial step within pre-processing , TDI comes with various configurations