A Computationally Efficient Surrogate Model for Generating High-Accuracy Intermediate to High Mass-Ratio Inspiral Waveforms
ORAL
Abstract
Gravitational wave signals from compact astrophysical sources such as those observed by LIGO and Virgo require a high-accuracy waveform model for signal analysis. Current inspiral-merger-ringdown (IMR) models are calibrated only up to moderate mass ratios, thereby limiting their applicability to signals from high-mass ratio binary systems. We introduce reduced-order surrogate models for gravitational waveforms including several harmonic modes and with mass-ratios varying from 3 to 10,000 and with spin up to 0.8 on the primary black hole, thus vastly expanding the parameter range beyond current surrogate IMR models. Our model is trained on waveforms generated by point-particle black hole perturbation theory (ppBHPT) both for large and comparable mass-ratio binaries. We calibrate the model to spin-aligned numerical relativity simulations in the comparable mass-ratio regime. Our waveforms in the comparable mass-ratio regime agree surprisingly well with those from full numerical relativity after this calibration step. These results will enable data analysis studies in the high-mass ratio regime, including potential intermediate mass-ratio signals from LIGO/Virgo and extreme-mass ratio events of interest to the future space-based observatory LISA.
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Publication: PLANNED:<br><br>S. E. Field, K. Gonzalez, T. Islam, G. Khanna, N. E. M. Rifat, K. Rink, V. Varma, A Surrogate Model for Gravitational Wave Signals from Comparable- to Large- Mass-Ratio Black Hole Binaries with Spin, January 2022 (In prep).
Presenters
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Katie Rink
University of Massachusetts Dartmouth
Authors
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Katie Rink
University of Massachusetts Dartmouth
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Scott E Field
University of Massachusetts Dartmouth
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Kevin Gonzalez-Quesada
University of Massachusetts Dartmouth
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Tousif Islam
University of Massachusetts Dartmouth
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Gaurav Khanna
University of Massachusetts Dartmouth
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Nur-E-Mohammad Rifat
University of Massachusetts Dartmouth
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Vijay Varma
Cornell University