Untangling Extreme Mass Ratio Inspirals and Massive Black Hole Binaries in LISA Data
Abstract
The Laser Interferometer Space Antenna (LISA) will observe sources in the millihertz regime, such as Galactic Binaries (GBs), Massive Black Hole Binaries (MBHBs), and Extreme Mass Ratio Inspirals (EMRIs), so its data stream will be a superposition of an unknown number of gravitational-wave sources. We consider the joint inference of various representative combinations of MBHB and EMRI signals in LISA data (with assumed instrumental noise and stochastic GB foreground). Our Bayesian analyses confirm the expectation of weak inter-source coupling between EMRIs, and between EMRIs and MBHBs, despite strong intra-source parameter correlations. Weak EMRI-MBHB coupling enables a hierarchically sequential approach to EMRI and MBHB inference, rather than fully joint inference. We further show that imperfect MBHB subtraction, arising from missing modes or overlapping MBHBs, has only a weak impact on EMRI inference. For multiple MBHBs, we mitigate inference errors through a joint MBHB fit. We demonstrate this strategy on simulated LISA data containing noise, three MBHBs, and three EMRIs, jointly fitting and subtracting the MBHBs before sequentially inferring the EMRIs - ultimately observing little to no bias on all recovered astrophysical parameters. The notions described throughout our work are highly relevant to informing optimal strategies for the global-fit-based pipelines that are currently being developed for LISA.
Related
- Devoured by a Hairy Gargantua: Probing Massive Scalar Charges with Non-minimal Curvature Coupling with Extreme-Mass-Ratio Inspirals
- Massive scalar fields in eccentric regime: Detectability and constraints from LISA observations of extreme mass-ratio inspirals
- Pitching Cosmic Curveballs: Environmental Effects on Extreme-Mass-Ratio Inspirals with Spinning Secondaries
- Self-lensing of electromagnetic counterparts to extreme mass ratio inspirals
- A Systematic Study of Resonance-Driven Flux Modifications in Extreme-Mass-Ratio Inspirals
BibTeX
@misc{agrawal2026untangling,
title = {{Untangling Extreme Mass Ratio Inspirals and Massive Black Hole Binaries in LISA Data}},
author = {Rishav Agrawal and Clémence Allietta and Ollie Burke and Alvin J. K. Chua},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.27642},
eprint = {2610.27642},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.27642},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Untangling Extreme Mass Ratio Inspirals and Massive Black Hole Binaries in LISA Data
AU - Rishav Agrawal
AU - Clémence Allietta
AU - Ollie Burke
AU - Alvin J. K. Chua
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2610.27642
UR - https://arxiv.org/abs/2610.27642
AB - The Laser Interferometer Space Antenna (LISA) will observe sources in the millihertz regime, such as Galactic Binaries (GBs), Massive Black Hole Binaries (MBHBs), and Extreme Mass Ratio Inspirals (EMRIs), so its data stream will be a superposition of an unknown number of gravitational-wave sources. We consider the joint inference of various representative combinations of MBHB and EMRI signals in LISA data (with assumed instrumental noise and stochastic GB foreground). Our Bayesian analyses confirm the expectation of weak inter-source coupling between EMRIs, and between EMRIs and MBHBs, despite strong intra-source parameter correlations. Weak EMRI-MBHB coupling enables a hierarchically sequential approach to EMRI and MBHB inference, rather than fully joint inference. We further show that imperfect MBHB subtraction, arising from missing modes or overlapping MBHBs, has only a weak impact on EMRI inference. For multiple MBHBs, we mitigate inference errors through a joint MBHB fit. We demonstrate this strategy on simulated LISA data containing noise, three MBHBs, and three EMRIs, jointly fitting and subtracting the MBHBs before sequentially inferring the EMRIs - ultimately observing little to no bias on all recovered astrophysical parameters. The notions described throughout our work are highly relevant to informing optimal strategies for the global-fit-based pipelines that are currently being developed for LISA.
ER -