Search

Core-collapse supernovae (CCSNe) are a potential source for ground-based gravitational wave detectors, as their predicted emission peaks in the detectors' frequency band. Typical searches for gravitational wave bursts reconstruct signals using wavelets. However, as CCSN signals contain multiple complex features in the time-frequency domain, these techniques often struggle to reconstruct the entire signal. An alternative method developed in recent years involves applying principal component analysis (PCA) to a set of simulated CCSN models. This technique enables model selection between astrophysical CCSN models as well as waveform reconstruction. However, PCA faces its own difficulties, such as being unable to reconstruct signals longer than the simulations; many CCSN simulations are stopped before the emission peaks due to insufficient computational resources. In this study, we show how combining PCA with dynamic time warping improves the reconstruction of CCSN gravitational wave signals in Gaussian noise characteristic of Advanced LIGO at design sensitivity. For the waveforms used in this analysis, we find that the number of PCs needed to represent 90% of the data is reduced from nine to four by applying dynamic time warping and that the match between the original and reconstructed waveforms improves for signal-to-noise ratios in the range [0, 50]. [ABSTRACT FROM AUTHOR]

The next generation of gravitational wave detectors will improve the detection prospects for gravitational waves from core-collapse supernovae. The complex astrophysics involved in core-collapse supernovae pose a significant challenge to modeling such phenomena. The Supernova Model Evidence Extractor (SMEE) attempts to capture the main features of gravitational wave signals from core-collapse supernovae by using numerical relativity waveforms to create approximate models. These models can then be used to perform Bayesian model selection to determine if the targeted astrophysical feature is present in the gravitational wave signal. In this paper, we extend SMEE's model selection capabilities to include features in the gravitational wave signal that are associated with g-modes and the standing accretion shock instability. For the first time, we test SMEE's performance using simulated data for planned future detectors, such as the Einstein Telescope, Cosmic Explorer, and LIGO Voyager. Further to this, we show how the performance of SMEE is improved by creating models from the spectrograms of supernova waveforms instead of their time-series waveforms that contain stochastic features. In third generation detector configurations, we find that about 50% of neutrino-driven simulations were detectable at 100 kpc, and 10% at 275 kpc. The explosion mechanism was correctly determined for all detected signals. [ABSTRACT FROM AUTHOR]

A detection of a core-collapse supernova signal with an advanced LIGO and Virgo gravitational-wave detector network will allow us to measure astrophysical parameters of the source. In real advanced gravitational-wave detector data, there are transient noise artifacts that may mimic a true gravitational-wave signal. In this paper, we outline a procedure implemented in the supernova model evidence extractor (SMEE) that determines if a core-collapse supernova signal candidate is a noise artefact, a rapidly rotating core-collapse supernova signal, or a neutrino explosion mechanism core-collapse supernova signal. Further, we use the latest available three-dimensional gravitational-wave core-collapse supernova simulations, and we outline a new procedure for the rejection of background noise transients when only one detector is operational. We find the minimum signal-to-noise ratio needed to detect all waveforms is reduced when using three-dimensional waveforms as signal models. [ABSTRACT FROM AUTHOR]

A detection of a core-collapse supernova (CCSN) gravitational-wave (GW) signal with an Advanced LIGO and Virgo detector network may allow us to measure astrophysical parameters of the dying massive star. GWs are emitted from deep inside the core, and, as such, they are direct probes of the CCSN explosion mechanism. In this study, we show how we can determine the CCSN explosion mechanism from a GW supernova detection using a combination of principal component analysis and Bayesian model selection. We use simulations of GW signals from CCSN exploding via neutrino-driven convection and rapidly rotating core collapse. Previous studies have shown that the explosion mechanism can be determined using one LIGO detector and simulated Gaussian noise. As real GW detector noise is both nonstationary and non-Gaussian, we use real detector noise from a network of detectors with a sensitivity altered to match the advanced detectors design sensitivity. For the first time, we carry out a careful selection of the number of principal components to enhance our model selection capabilities. We show that with an advanced detector network we can determine if the CCSN explosion mechanism is driven by neutrino convection for sources in our Galaxy and rapidly-rotating core collapse for sources out to the Large Magellanic Cloud. [ABSTRACT FROM AUTHOR]

This paper presents the Summed Parallel Infinite Impulse Response (SPIIR) pipeline used for public alerts during the third advanced LIGO and Virgo observation run (O3 run). The SPIIR pipeline uses infinite impulse response (IIR) filters to perform extremely low-latency matched filtering and this process is further accelerated with graphics processing units (GPUs). It is the first online pipeline to select candidates from multiple detectors using a coherent statistic based on the maximum network likelihood ratio statistic principle. Here we simplify the derivation of this statistic using the singular-value-decomposition (SVD) technique and show that single-detector signal-to-noise ratios from matched filtering can be directly used to construct the statistic. Coherent searches are in general more computationally challenging than coincidence searches due to extra search over sky direction parameters. The search over sky directions follows an embarrassing parallelization paradigm and has been accelerated using GPUs. The detection performance is reported using a segment of public data from LIGO-Virgo's second observation run. We demonstrate that the median latency of the SPIIR pipeline is less than 9 seconds, and present an achievable road map to reduce the latency to less than 5 seconds. During the O3 online run, SPIIR registered triggers associated with 38 of the 56 nonretracted public alerts. The extreme low-latency nature makes it a competitive choice for joint time-domain observations, and offers the tantalizing possibility of making public alerts prior to the merger phase of binary coalescence systems involving at least one neutron star. [ABSTRACT FROM AUTHOR]

We performed a detailed analysis of the detectability of a wide range of gravitational waves derived from core-collapse supernova simulations using gravitational-wave detector noise scaled to the sensitivity of the upcoming fourth and fifth observing runs of the Advanced LIGO, Advanced Virgo, and KAGRA. We use the coherent WaveBurst algorithm, which was used in the previous observing runs to search for gravitational waves from core-collapse supernovae. As coherent WaveBurst makes minimal assumptions on the morphology of a gravitational-wave signal, it can play an important role in the first detection of gravitational waves from an event in the Milky Way. We predict that signals from neutrino-driven explosions could be detected up to an average distance of 10 kpc, and distances of over 100 kpc can be reached for explosions of rapidly-rotating progenitor stars. An estimated minimum signal-to-noise ratio of 10-25 is needed for the signals to be detected. We quantify the accuracy of the waveforms reconstructed with coherent WaveBurst and we determine that the most challenging signals to reconstruct are those produced in long-duration neutrino-driven explosions, and models that form black holes a few seconds after the core bounce. [ABSTRACT FROM AUTHOR]