LIGO Document T2300219-v1

Identifying Correlations in Precessing Gravitational-Wave Signals with Machine Learning

Document #:
LIGO-T2300219-v1
Document type:
T - Technical notes
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Abstract:
Documents from SURF23 student Karen Kang, Amherst College

Binary binary hole (BBH) spins provide important insights on the formation environments, evolutionary history, and dynamics of these objects, which could be of interest of the broader astrophysics community. We would like to better measure signals for highly massive (total mass > 100 solar masses), highly spinning BBH systems, which are subject to spurious measurements due to their very short duration and low bandwidth. The astrophysical parameters of gravitational wave (GW) sources are extracted from match filtering observed signals to templated waveforms. The waveforms which include the most underlying physics are those generated with numerical relativity (NR). However, different parameters of NR simulation, such as mass and spin, can lead to extremely similar waveforms. In such cases, the analysis pipeline will not be able to distinguish potential sources. We are interested in constructing a neural network to study the correlations between different parameters of waveforms with spin precession and to identify potential ways to break such degeneracies. The results produced by this network would inform us the measurability of spin parameters from inferred waveform signals.

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