Assessing the performance of future space-based detectors: astrophysical foregrounds and individual sources

TL;DR

The paper investigates how future space-based gravitational-wave detectors—μAres, AMIGO, and the Decihertz Observatory—perform in the presence of astrophysical foregrounds within the milli-Hz to deci-Hz regime. It employs a self-consistent framework that integrates MBHBs, EMRIs, SOBBHs, GBs, and extragalactic DWDs through iterative subtraction to estimate the unresolved background and to quantify the number and properties of resolvable sources for each detector. Key findings show that μAres excels at long, early MBHB inspirals and DO offers strong deci-Hz performance with extensive high-redshift and multi-band potential, while Galactic foregrounds limit gains for AMIGO and LISA in certain bands; the extragalactic DWD foreground often dominates the stochastic background. The results inform mission design and multi-messenger strategies, emphasizing the need for realistic foreground subtraction and highlighting the potential of cross-band observations and cosmological backgrounds.

Abstract

The space mission LISA (Laser Interferometer Space Antenna), scheduled for launch in 2035, aims to detect gravitational wave (GW) signals in the milli-Hz band. In the context of ESA Voyage 2050 Call for new mission concepts, other frequency ranges are explored by the Gravitational-Wave Space 2050 Working Group to conceive new proposals for a post-LISA space-based detector. In this work, we give a preliminary estimate of the observational potential of three mission designs proposed in the literature, namely $μ$Ares, AMIGO and the Decihertz Observatory. The analysis framework includes astrophysical GW sources such as massive black hole binaries, extreme mass-ratio inspirals and compact binaries such as stellar black holes and white dwarfs. For each detector, we first present a consistent computation of the unresolved gravitational wave background (GWB) produced by the sum of all anticipated astrophysical populations, using an iterative subtraction algorithm. We then investigate which types of systems are the most appealing, by measuring the number of GW signals detected and exploring the source properties.

Figures (17)

• Figure 1: Amplitude spectral density of the sensitivity curves for the four detectors.
• Figure 2: Flowchart of the code's algorithm.
• Figure 3: Each panel shows the characteristic strain of the total unresolved GWB (black dashed line) present in the detector, computed in the HS scenario. The various contributions are represented by colored solid lines, while the solid black line denotes the instrumental PSD. The LISA noise curve is plotted in gray for comparison.
• Figure 4: Each panel shows the characteristic strain of the total unresolved GWB (dashed black line) present in the detector, computed in the LS scenario. The colored solid lines denote the contributions from different source populations. The instrumental PSD of each mission design is plotted in solid black, with the LISA one in gray for comparison.
• Figure 5: SNR distribution for the MBHB catalog in the HS scenario.