Natural polynomials for Schwarzschild and Kerr quasinormal modes are Pollaczek-Jacobi polynomials with complex parameters, with recurrence peaking at the physical overtone index for Schwarzschild.
Science with the space-based interferometer eLISA. I: Supermassive black hole binaries
9 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We compare the science capabilities of different eLISA mission designs, including four-link (two-arm) and six-link (three-arm) configurations with different arm lengths, low-frequency noise sensitivities and mission durations. For each of these configurations we consider a few representative massive black hole formation scenarios. These scenarios are chosen to explore two physical mechanisms that greatly affect eLISA rates, namely (i) black hole seeding, and (ii) the delays between the merger of two galaxies and the merger of the black holes hosted by those galaxies. We assess the eLISA parameter estimation accuracy using a Fisher matrix analysis with spin-precessing, inspiral-only waveforms. We quantify the information present in the merger and ringdown by rescaling the inspiral-only Fisher matrix estimates using the signal-to-noise ratio from non-precessing inspiral-merger-ringdown phenomenological waveforms, and from a reduced set of precessing numerical relativity/post-Newtonian hybrid waveforms. We find that all of the eLISA configurations considered in our study should detect some massive black hole binaries. However, configurations with six links and better low-frequency noise will provide much more information on the origin of black holes at high redshifts and on their accretion history, and they may allow the identification of electromagnetic counterparts to massive black hole mergers.
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Dynamical dark energy imprints O(1) shifts on black hole quasi-normal modes via cosmological hair, enabling constraints at 10^{-2} (LVK) to 10^{-4} (LISA) precision using the cubic Galileon as example.
Conditional normalizing flows perform likelihood-free parameter estimation for single and overlapping LISA galactic binaries, generating thousands of posterior samples per second after training on simulations.
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
Simulations of dynamical channels predict ~36 eccentric stellar-mass BBHs detectable by LISA in the Milky Way at SNR>1 over 10 years, a local merger rate of ~9 Gpc^{-3} yr^{-1}, and hundreds of faint extragalactic mHz sources.
A multi-parameter formalism is developed to describe asymmetric binaries in general matter distributions by perturbing around Schwarzschild and reducing metric and fluid perturbations to wave equations similar to the vacuum case.
Eccentricity influences LISA binary counts via peak frequency, required density for LIGO rate match, and SNR reduction, enabling formation channel discrimination through frequency-dependent number counts without direct eccentricity measurement.
Simulations indicate joint Taiji+LISA analysis of five SLGW events yields H0 95% credible interval uncertainties of 0.11 (source redshift unknown) or 0.042 (source redshift known).
A review of existing waveform models for LISA sources and the challenges that must still be overcome.
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Eccentricity Without Measuring Eccentricity: Discriminating Among Stellar Mass Black Hole Binary Formation Channels
Eccentricity influences LISA binary counts via peak frequency, required density for LIGO rate match, and SNR reduction, enabling formation channel discrimination through frequency-dependent number counts without direct eccentricity measurement.