Extreme mass ratio inspiral data analysis with a phenomenological waveform

Abstract

Extreme mass ratio inspirals (EMRIs) [capture and inspiral of a compact stellar mass object into a massive black hole] are among the most interesting objects for gravitational wave astronomy. It is a very challenging task to detect those sources with the accurate estimation parameters of binaries primarily due to a large number of secondary maxima on the likelihood surface. Search algorithms based on matched filtering require computation of the gravitational waveform hundreds of thousands of times, which is currently not feasible with the most accurate (faithful) models of EMRIs. Here we propose to use a phenomenological template family which covers a large range of EMRI parameter space. We use these phenomenological templates to detect the signal in the simulated data and then, assuming a particular EMRI model, estimate the physical parameters of the binary. We have separated the detection problem, which is done in a model-independent way, from the parameter estimation. For the latter one, we need to adopt the model for inspiral in order to map phenomenological parameters onto the physical parameters characterizing EMRIs.

Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks

“…Both templatebased algorithms and template-free methods have been proposed to detect the EMRI signals. The former includes the semi-coherent method [29] and F-statistic algorithm [30,31], and the latter includes the timefrequency algorithm [32][33][34][35]. On the parameter estimation side, methods like Metropolis-Hastings search [36,37], parallel tempering MCMC [38], and Gaussian process [39,40] have been implemented.…”

Characterization of the stochastic signal originating from compact binaries populations as measured by LISA

“…This implies that all GW signals from various sources will be have to be fit for and characterized simultaneously. The sources generating overlapping signals include Super-Massive Black Hole Binaries (SMBHBs) [2,3], Stellar-mass Black Hole Binaries (SBBHs) [4][5][6][7][8][9], ultra-Compact Binaries originating in our Galaxy (CGBs) [10][11][12][13], Extreme Mass Ratio Inspirals (EMRIs) [14][15][16][17], and a Stochastic GW Background (SGWB) that may originate from cosmological sources [18,19]. The number and density of GW sources in the LISA band presents a data analysis challenge of producing a global fit [20], simultaneously detecting and classifying overlapping signals, which is often referred to as the "source confusion problem.…”

Rapid Generation of Fully Relativistic Extreme-Mass-Ratio-Inspiral Waveform Templates for LISA Data Analysis

“…Calculations from black-hole perturbation theory, and in particular from the ongoing gravitational self-force program [7], are on target to produce EMRI waveforms that meet the accuracy requirements of LISA science [8,9]. Such models are computationally intensive, and hence ill suited for direct use in analysis algorithms that are tailored to the EMRI problem [10][11][12][13][14]. As in the case of numericalrelativity waveforms for comparable-mass binaries, selfforce waveforms must be supplemented and approximated by template models that are (i) efficiency oriented, (ii) extensive in their description of both intrinsic and extrinsic effects, and (iii) end to end from source parameters to detector response.…”