New method for enhanced efficiency in detection of gravitational waves from supernovae using coherent network of detectors

Abstract

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 burstlike 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 significant improvement in detection statistics.

Detecting and reconstructing gravitational waves from the next Galactic core-collapse supernova in the Advanced Detector Era

“…Being non-deterministic, matched filtering cannot be used and methods should allow for uncertainties in the signal models. Among these methods are Principal Component Analysis [27][28][29][30], Bayesian inference [28][29][30][31][32], Machine Learning [33][34][35][36][37], denoising techniques [38], and others [39][40][41]. These methods apply the knowledge of CCSN models to different degrees.…”

Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae

“…In recent times, ML along with the special class of deep learning (DL) models have attracted a lot of attention to tackle several problems in the realm of GWs [24], for instance, to discriminate between noise and GW signals either from binary systems [25][26][27][28][29] or from CCSNe [30][31][32][33], and to identify and remove transient noise events using strain data or auxiliary channels [34][35][36][37]. Notably, ML models also have been used to enhance cWB performance, in specific, to distinguish between glitches and GW signals from BBH [38], to construct a statistical veto based on the recognition of noise events to improve the detection efficiencies of GWs from BBH [39], and to achieve higher detection sensitivity of GW signals from CCSNe using signal enhancement [40,41]. ML models, in specific genetic programming algorithms, had been previously investigated to discriminate CCSNe GW signals from noise transients for the case of single detector searches [42].…”

Circular polarization of gravitational waves from non-rotating supernova cores: a new probe into the pre-explosion hydrodynamics

“… See Logue et al (2012);Gossan et al (2016);Mukherjee et al (2017) for representative studies using other pipelines.MNRAS 000, 1-5(2018) …”