Using predictions from three-dimensional (3D) hydrodynamics simulations of core-collapse supernovae (CCSNe), we present a coherent network analysis to detection, reconstruction, and the source localization of the gravitational-wave (GW) signals. We u
se the {tt RIDGE} pipeline for the analysis, in which the network of LIGO Hanford, LIGO Livingston, VIRGO, and KAGRA is considered. By combining with a GW spectrogram analysis, we show that several important hydrodynamics features in the original waveforms persist in the waveforms of the reconstructed signals. The characteristic excess in the spectrograms originates not only from rotating core-collapse, bounce and the subsequent ring down of the proto-neutron star (PNS) as previously identified, but also from the formation of magnetohydrodynamics jets and non-axisymmetric instabilities in the vicinity of the PNS. Regarding the GW signals emitted near at the rotating core bounce, the horizon distance extends up to $sim$ 18 kpc for the most rapidly rotating 3D model in this work. Following the rotating core bounce, the dominant source of the GW emission shifts to the non-axisymmetric instabilities. The horizon distances extend maximally up to $sim$ 40 kpc seen from the spin axis. With an increasing number of 3D models trending towards explosion recently, our results suggest that in addition to the best studied GW signals due to rotating core-collapse and bounce, the time is ripe to consider how we can do science from GWs of CCSNe much more seriously than before. Particularly the quasi-periodic signals due to the non-axisymmetric instabilities and the detectability should deserve further investigation to elucidate the inner-working of the rapidly rotating CCSNe.
The observation of gravitational waves with a global network of interferometric detectors such as advanced LIGO, advanced Virgo, and KAGRA will make it possible to probe into the nature of space-time structure. Besides Einsteins general theory of rel
ativity, there are several theories of gravitation that passed experimental tests so far. The gravitational-wave observation provides a new experimental test of alternative theories of gravity because a gravitational wave may have at most six independent modes of polarization, of which properties and number of modes are dependent on theories of gravity. This paper proposes a method to reconstruct the independent modes of polarization in time-series data of an advanced detector network. Since the method does not rely on any specific model, it gives model-independent test of alternative theories of gravity.
Significant progress has been made in the development of an international network of gravitational wave detectors, such as TAMA300, LIGO, VIRGO, and GEO600. For these detectors, one of the most promising sources of gravitational waves are core collap
se supernovae especially in our Galaxy. Recent simulations of core collapse supernovae, rigorously carried out by various groups, show that the features of the waveforms are determined by the rotational profiles of the core, such as the rotation rate and the degree of the differential rotation prior to core-collapse. Specifically, it has been predicted that the sign of the second largest peak in the gravitational wave strain signal is negative if the core rotates cylindrically with strong differential rotation. The sign of the second peak could be a nice indicator that provides us with information about the angular momentum distribution of the core, unseen without gravitational wave signals. Here we present a data analysis procedure aiming at the detection of the second peak using a coherent network analysis and estimate the detection efficiency when a supernova is at the sky location of the Galactic center. The simulations showed we were able to determine the sign of the second peak under an idealized condition of a network of gravitational wave detectors if a supernova occurs at the Galactic center.
Sco X-1, the brightest low mass X-ray binary, is likely to be a source for gravitational wave emission. In one mechanism, emission of a gravitational wave arrests the increase in spin frequency due to the accretion torque in a low mass X-ray binary.
Since the gravitational waveform is unknown, a detection method assuming no apriori knowledge of the signal is preferable. In this paper, we propose to search for a gravitational wave from Sco X-1 using a {{it source tracking}} method based on a coherent network analysis. In the method, we combine data from several interferometric gravitational wave detectors taking into account of the direction to Sco X-1, and reconstruct two polarization waveforms at the location of Sco X-1 in the sky as Sco X-1 is moving. The source tracking method opens up the possibility of searching for a wide variety of signals. We perform Monte Carlo simulations and show results for bursts, modeled, short duration periodic sources using a simple excess power and a matched filter method on the reconstructed signals.
Pulsar glitches are a potential source of gravitational waves for current and future interferometric gravitational wave detectors. Some pulsar glitch events were observed by radio and X-ray telescopes during the fifth LIGO science run. It is expected
that glitches from these same pulsars should also be seen in the future. We carried out Monte Carlo simulations to estimate the sensitivity of possible gravitational wave signals associated with a pulsar glitch using a coherent network analysis method. We show the detection efficiency and evaluate the reconstruction accuracy of gravitational waveforms using a matched filter analysis on the estimated gravitational waveforms from the coherent analysis algorithm.
Searches for gravitational wave bursts that are triggered by the observation of astronomical events require a different mode of analysis than all-sky, blind searches. For one, much more prior information is usually available in a triggered search whi
ch can and should be used in the analysis. Second, since the data volume is usually small in a triggered search, it is also possible to use computationally more expensive algorithms for tasks such as data pre-processing that can consume significant computing resources in a high data-volume un-triggered search. From the statistical point of view, the reduction in the parameter space search volume leads to higher sensitivity than an un-triggered search. We describe here a data analysis pipeline for triggered searches, called {tt RIDGE}, and present preliminary results for simulated noise and signals.
