LIGO Document P1400116-v8
- Supernovae in our universe are potential sources of Gravitational Waves (GW) that could be
detected in a network of GW detectors like LIGO and Virgo. Core-collapse supernovae are rare,
but the associated gravitational radiation is likely to carry profuse information about the underlying
processes driving the supernovae. Calculations based on analytic models predict GW energies within
the detection range of the Advanced LIGO detectors, out to tens of Mpc for certain types of signals
e.g. coalescing binary neutron stars. For supernovae however,the corresponding distances are much
less. Thus, methods that can improve the sensitivity of searches for GW signals from supernovae
are desirable, especially in the advanced detector era. Several methods have been proposed based on
various likelihood-based regulators that work on data from a network of detectors to detect burst-
like signals (as is the case for signals from supernovae) from potential GW sources. To address
this problem, we have developed an analysis pipeline based on a method of noise reduction known
as the Harmonic Regeneration Noise Reduction (HRNR) algorithm. To demonstrate the method,
sixteen supernova waveforms from the Murphy et al. 2009 catalog have been used in presence of
LIGO science data. A comparative analysis is presented to show detection statistics for a standard
network analysis as commonly used in GW pipelines and the same by implementing the new method
in conjunction with the network. The result shows signi cant improvement in detection statistics.
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