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We present a systematic comparison of the binary black hole (BBH) signal waveform reconstructed by two independent and complementary approaches used in LIGO and Virgo source inference: a template-based analysis, and a morphology-independent analysis. We apply the two approaches to real events and to two sets of simulated observations made by adding simulated BBH signals to LIGO and Virgo detector noise. The first set is representative of the 10 BBH events in the first Gravitational Wave Transient Catalog (GWTC-1). The second set is constructed from a population of BBH systems with total mass and signal strength in the ranges that ground based detectors are typically sensitive. We find that the reconstruction quality of the GWTC-1 events is consistent with the results of both sets of simulated signals. We also demonstrate a simulated case where the presence of a mismodelled effect in the observed signal, namely higher order modes, can be identified through the morphology-independent analysis. This study is relevant for currently progressing and future observational runs by LIGO and Virgo.
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 c
Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the s
Gravitational radiation is properly defined only at future null infinity ($scri$), but in practice it is estimated from data calculated at a finite radius. We have used characteristic extraction to calculate gravitational radiation at $scri$ for the
Some astrophysical sources of gravitational waves can produce a memory effect, which causes a permanent displacement of the test masses in a freely falling gravitational-wave detector. The Christodoulou memory is a particularly interesting nonlinear
[Abridged] We introduce an improved version of the Eccentric, Non-spinning, Inspiral-Gaussian-process Merger Approximant (ENIGMA) waveform model. We find that this ready-to-use model can: (i) produce physically consistent signals when sampling over 1