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An optimized PyCBC search for gravitational waves from intermediate-mass black hole mergers

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 Added by Koustav Chandra
 Publication date 2021
  fields Physics
and research's language is English




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The detection of intermediate-mass black holes (IMBHs) i.e. those with mass $sim 100$-$10^5 M_odot$, is an emerging goal of gravitational-wave (GW) astronomy with wide implications for cosmology and tests of strong-field gravity. Current PyCBC-based searches for compact binary mergers, which matched filter the detector data against a set of template waveforms, have so far detected or confirmed several GW events. However, the sensitivity of these searches to signals arising from mergers of IMBH binaries is not optimal. Here, we present a new optimised PyCBC-based search for such signals. Our search benefits from using a targeted template bank, stricter signal-noise discriminators and a lower matched-filter frequency cut-off. In particular, for a population of simulated signals with isotropically distributed spins, we improve the sensitive volume-time product over previous PyCBC-based searches, at an inverse false alarm rate of 100 years, by a factor of 1.5 to 3 depending on the total binary mass. We deploy this new search on Advanced LIGO-Virgo data from the first half of the third observing run. The search does not identify any new significant IMBH binaries but does confirm the detection of the short-duration GW signal GW190521 with a false alarm rate of 1 in 727 years.



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We present the results of a weakly modeled burst search for gravitational waves from mergers of non-spinning intermediate mass black holes (IMBH) in the total mass range 100--450 solar masses and with the component mass ratios between 1:1 and 4:1. The search was conducted on data collected by the LIGO and Virgo detectors between November of 2005 and October of 2007. No plausible signals were observed by the search which constrains the astrophysical rates of the IMBH mergers as a function of the component masses. In the most efficiently detected bin centered on 88+88 solar masses, for non-spinning sources, the rate density upper limit is 0.13 per Mpc^3 per Myr at the 90% confidence level.
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We apply machine learning methods to build a time-domain model for gravitational waveforms from binary black hole mergers, called mlgw. The dimensionality of the problem is handled by representing the waveforms amplitude and phase using a principal component analysis. We train mlgw on about $mathcal{O}(10^3)$ TEOBResumS and SEOBNRv4 effective-one-body waveforms with mass ratios $qin[1,20]$ and aligned dimensionless spins $sin[-0.80,0.95]$. The resulting models are faithful to the training sets at the ${sim}10^{-3}$ level (averaged on the parameter space). The speed up for a single waveform generation is a factor 10 to 50 (depending on the binary mass and initial frequency) for TEOBResumS and approximately an order of magnitude more for SEOBNRv4. Furthermore, mlgw provides a closed form expression for the waveform and its gradient with respect to the orbital parameters; such an information might be useful for future improvements in GW data analysis. As demonstration of the capabilities of mlgw to perform a full parameter estimation, we re-analyze the public data from the first GW transient catalog (GWTC-1). We find broadly consistent results with previous analyses at a fraction of the cost, although the analysis with spin aligned waveforms gives systematic larger values of the effective spins with respect to previous analyses with precessing waveforms. Since the generation time does not depend on the length of the signal, our model is particularly suitable for the analysis of the long signals that are expected to be detected by third-generation detectors. Future applications include the analysis of waveform systematics and model selection in parameter estimation.
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