What the study found
The study found that extreme mass ratio inspiral, or EMRI, parameter estimates in the Laser Interferometer Space Antenna can be biased by streams of transient noise artifacts called glitches. For moderately mitigated glitch streams, the biases were negligible to minor, while weaker mitigation allowed larger biases.
Why the authors say this matters
The authors conclude that EMRI inference is notably more robust to glitches than inference for some other sources, such as massive black hole binaries. They also stress that some glitch modeling and mitigation remains essential for unbiased EMRI analyses in the LISA era.
What the researchers tested
The researchers used simulated LISA observations with injected EMRIs and streams of shapelet-based glitches drawn from the LISA Pathfinder catalog. They estimated glitch-induced parameter biases and uncertainties with a Fisher-matrix-based analysis and checked its accuracy with Markov-chain Monte Carlo.
What worked and what didn't
Moderately mitigated glitch streams, containing only glitches up to moderate signal-to-noise ratios, produced biases of about 0.04σ to 0.6σ in inferred EMRI parameters. Weakly mitigated streams with higher-signal-to-noise events could produce biases nearing 1σ. The abstract reports that the Fisher-matrix approach was verified with Markov-chain Monte Carlo, but it does not give further details here.
What to keep in mind
The summary only describes simulated observations, so the reported results are limited to that setting. The abstract does not provide additional limitations beyond the need for at least some glitch modeling and mitigation.
Key points
- The study examined how glitch streams affect EMRI parameter estimation in LISA.
- Moderately mitigated glitch streams caused only negligible to minor biases, about 0.04σ to 0.6σ.
- Weakly mitigated glitch streams with higher-signal-to-noise events could produce biases nearing 1σ.
- The authors say EMRI inference is more robust to glitches than inference for massive black hole binaries.
- Some glitch modeling and mitigation is still described as essential for unbiased EMRI analyses.
Disclosure
- Research title:
- Glitches cause small to minor bias in EMRI inference
- Authors:
- Amin Boumerdassi, Matthew C. Edwards, Avi Vajpeyi, Ollie Burke
- Institutions:
- Institut National de Physique Nucléaire et de Physique des Particules, Université Fédérale de Toulouse Midi-Pyrénées, University of Auckland, University of Auckland, University of Auckland, University of Glasgow
- Publication date:
- 2026-04-22
- DOI:
- 10.1103/9678-764y
- OpenAlex record:
- View
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