First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals

Abstract

This work investigates the impact of streams of transient, non-Gaussian noise artifacts or "glitches" on the parameter estimation of extreme mass ratio inspirals (EMRI) in the Laser Interferometer Space Antenna (LISA). Glitches cause biased and less precise inference for short-duration signals such as massive black hole binaries, but their effect on long-lived sources such as EMRIs has not been quantified. Using simulated LISA observations containing injected EMRIs and streams of shapelet-based glitches drawn from the LISA Pathfinder catalog, we estimate the glitch-induced parameter biases and uncertainties through a Fisher-matrix-based analysis whose accuracy we verify with Markov-Chain Monte Carlo. We find that moderately mitigated glitch streams i.e. ones containing only glitches of up to moderate SNRs ($\rho \lesssim 90$) induce negligible to minor biases $[\sim0.04\sigma ,\sim0.6\sigma]$ in the inferred EMRI parameters. In contrast, weakly mitigated glitch streams containing higher-SNR events ($\rho \lesssim 400$) can produce biases nearing $1\sigma$. These results demonstrate that, when compared to inference of other sources such as massive black hole binaries, EMRI inference is notably more robust to glitches. We stress that at least some amount of glitch modeling and mitigation remains essential for unbiased EMRI analyses in the LISA era.