Moderately mitigated glitch streams induce negligible to minor biases (0.04–0.6σ) in EMRI parameters while weakly mitigated streams with higher-SNR events can reach ~1σ biases, making EMRI inference more robust than for MBHBs.
Systematic errors in fast relativistic waveforms for Extreme Mass Ratio Inspirals
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Accurate modeling of \gls{EMRIs} is essential for extracting reliable information from future space-based gravitational wave observatories. Fast waveform generation frameworks adopt an offline/online architecture, where expensive relativistic computations (e.g. self-force and black hole perturbation theory) are performed offline, and waveforms are generated rapidly online via interpolation across a multidimensional parameter space. In this work, we investigate potential sources of error that result in systematic bias in these relativistic waveform models, focusing on radiation-reaction fluxes. Two key sources of systematics are identified: (i) the intrinsic inaccuracy of the flux data, for which we focus on the truncation of the multipolar mode sum, and (ii) interpolation errors from transitioning to the online stage. We quantify the impact of mode-sum truncation and analyze interpolation errors by using various grid structures and interpolation schemes. For circular orbits in Kerr spacetime with spins larger than $a \geq 0.9$, we find that $\ell_{\text{max}} \geq 30$ is required for the necessary accuracy. We also develop an efficient Chebyshev interpolation scheme, achieving the desired accuracy level with significantly fewer grid points compared to spline-based methods. For circular orbits in Kerr spacetimes, we demonstrate via Bayesian studies that interpolating the flux to a maximum global relative error that is equal to the small mass ratio is sufficient for parameter estimation purposes. For 4-year long quasi-circular EMRI signals with SNRs $= \mathcal{O}(100)$ and mass-ratios $10^{-4}-10^{-6}$, a global relative error of $10^{-6}$ yields mismatches $<10^{-3}$ and negligible parameter estimation biases.
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Near-identity averaging transformations applied to osculating orbital elements reduce the computational cost of eccentric EOB inspirals by up to two orders of magnitude while maintaining accuracy for moderate to large eccentricities at NNLO.
Computes scalar and tensor fluxes for eccentric EMRIs with massive scalars, quantifies dephasing, and shows via Fisher matrix that LISA can constrain scalar charge and mass.
LISA EMRIs can constrain deviations from Kerr equatorial symmetry to 10^{-2} and axial symmetry to 10^{-3} using Analytic Kludge waveforms and Fisher analysis.
citing papers explorer
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First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals
Moderately mitigated glitch streams induce negligible to minor biases (0.04–0.6σ) in EMRI parameters while weakly mitigated streams with higher-SNR events can reach ~1σ biases, making EMRI inference more robust than for MBHBs.
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Efficient Eccentric Effective-One-Body Dynamics via Near-Identity Averaging Transformations
Near-identity averaging transformations applied to osculating orbital elements reduce the computational cost of eccentric EOB inspirals by up to two orders of magnitude while maintaining accuracy for moderate to large eccentricities at NNLO.
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Massive scalar fields in eccentric regime: Detectability and constraints from LISA observations of extreme mass-ratio inspirals
Computes scalar and tensor fluxes for eccentric EMRIs with massive scalars, quantifies dephasing, and shows via Fisher matrix that LISA can constrain scalar charge and mass.
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Probing Kerr Symmetry Breaking with LISA Extreme-Mass-Ratio Inspirals
LISA EMRIs can constrain deviations from Kerr equatorial symmetry to 10^{-2} and axial symmetry to 10^{-3} using Analytic Kludge waveforms and Fisher analysis.