I. S. Heng

Status of the GEO600 detector

Of all the large interferometric gravitational-wave detectors, the German/British project GEO600 is the only one which uses dual recycling. During the four weeks of the international S4 data-taking run it reached an instrumental duty cycle of 97% with a peak sensitivity of 7 × 10−22 Hz−1/2 at 1 kHz. This paper describes the status during S4 and improvements thereafter.

The status of GEO 600

The GEO 600 laser interferometer with 600m armlength is part of a worldwide network of gravitational wave detectors. GEO 600 is unique in having advanced multiple pendulum suspensions with a monolithic last stage and in employing a signal recycled optical design. This paper describes the recent commissioning of the interferometer and its operation in signal recycled mode.

The transient gravitational-wave sky

Interferometric detectors will very soon give us an unprecedented view of the gravitational-wave sky, and in particular of the explosive and transient Universe. Now is the time to challenge our theoretical understanding of short-duration gravitational-wave signatures from cataclysmic events, their connection to more traditional electromagnetic and particle astrophysics, and the data analysis techniques that will make the observations a reality. This paper summarizes the state of the art, future science opportunities, and current challenges in understanding gravitational-wave transients.

Inferring core-collapse supernova physics with gravitational waves

Stellar collapse and the subsequent development of a core-collapse supernova explosion emit bursts of gravitational waves (GWs) that might be detected by the advanced generation of laser interferometer gravitational-wave observatories such as Advanced LIGO, Advanced Virgo, and LCGT. GW bursts from core-collapse supernovae encode information on the intricate multidimensional dynamics at work at the core of a dying massive star and may provide direct evidence for the yet uncertain mechanism driving supernovae in massive stars. Recent multidimensional simulations of core-collapse supernovae exploding via the neutrino, magnetorotational, and acoustic explosion mechanisms have predicted GW signals which have distinct structure in both the time and frequency domains. Motivated by this, we describe a promising method for determining the most likely explosion mechanism underlying a hypothetical GW signal, based on principal component analysis and Bayesian model selection. Using simulated Advanced LIGO noise and assuming a single detector and linear waveform polarization for simplicity, we demonstrate that our method can distinguish magnetorotational explosions throughout the Milky Way (D≲10  kpc) and explosions driven by the neutrino and acoustic mechanisms to D≲2  kpc. Furthermore, we show that we can differentiate between models for rotating accretion-induced collapse of massive white dwarfs and models of rotating iron core collapse with high reliability out to several kpc.

Rotating stellar core-collapse waveform decomposition: a principal component analysis approach

This paper introduces the use of principal component analysis (PCA) as a method to decompose the catalogues of gravitational waveforms to produce a set of orthonormal basis vectors. We apply PCA to a set of gravitational waveforms produced by rotating stellar core-collapse simulations and compare its basis vectors with those obtained through Gram–Schmidt decomposition. The comparison is made using the match parameter which quantifies how well each waveform is reconstructed by a set of basis vectors. The performance of the two methods is found to be comparable with 14 Gram–Schmidt basis vectors and 12 principal components required if we require all waveforms in the catalogue to be reconstructed with a match of 0.9 or better. Additionally, we observe that the chosen set of waveforms has very similar features, and a match of at least 0.7 can be obtained by decomposing only waveforms generated from simulations with A = 2. We discuss the implications of this observation and the advantages of eigen-decomposing waveform catalogues with PCA.

Bayesian reconstruction of gravitational wave burst signals from simulations of rotating stellar core collapse and bounce

Presented in this paper is a technique that we propose for extracting the physical parameters of a rotating stellar core collapse from the observation of the associated gravitational wave signal from the collapse and core bounce. Data from interferometric gravitational wave detectors can be used to provide information on the mass of the progenitor model, precollapse rotation, and the nuclear equation of state. We use waveform libraries provided by the latest numerical simulations of rotating stellar core collapse models in general relativity, and from them create an orthogonal set of eigenvectors using principal component analysis. Bayesian inference techniques are then used to reconstruct the associated gravitational wave signal that is assumed to be detected by an interferometric detector. Posterior probability distribution functions are derived for the amplitudes of the principal component analysis eigenvectors, and the pulse arrival time. We show how the reconstructed signal and the principal component analysis eigenvector amplitude estimates may provide information on the physical parameters associated with the core collapse event.

Maximising the detection probability of kilonovae associated with gravitational wave observations

Estimates of the source sky location for gravitational wave signals are likely span areas ranging up to hundreds of square degrees or more, making it very challenging for most telescopes to search for counterpart signals in the electromagnetic spectrum. To boost the chance of successfully observing such counterparts, we have developed an algorithm which optimizes the number of observing fields and their corresponding time allocations by maximizing the detection probability. As a proof-of-concept demonstration, we optimize follow-up observations targeting kilonovae using telescopes including CTIO-Dark Energy Camera, Subaru-HyperSuprimeCam, Pan-STARRS and Palomar Transient Factory. We consider three simulated gravitational wave events with 90% credible error regions spanning areas from ~30 deg^2 to ~300 deg^2. Assuming a source at 200 Mpc, we demonstrate that to obtain a maximum detection probability, there is an optimized number of fields for any particular event that a telescope should observe. To inform future telescope design studies, we present the maximum detection probability and corresponding number of observing fields for a combination of limiting magnitudes and fields-of-view over a range of parameters. We show that for large gravitational wave error regions, telescope sensitivity rather than field-of-view, is the dominating factor in maximizing the detection probability.

Classification methods for noise transients in advanced gravitational-wave detectors

Noise of non-astrophysical origin will contaminate science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and Advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time-frequency scan of each event. Here, we present three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on Principal Component Analysis. They are Principal Component Analysis for Transients (PCAT), and an adaptation of LALInference Burst (LIB). The third algorithm is a combination of an event generator called Wavelet Detection Filter (WDF) and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, SNR and waveform morphology.

First steps towards characterizing the hierarchical algorithm for curves and ridges pipeline

The hierarchical algorithm for curves and ridges is a variation of the burst gravitational wave search algorithm known as TFClusters. In this paper, we examine the detection efficiency of the hierarchical algorithm for curves and ridges by injecting sine-Gaussian burst waveforms with four different central frequencies into data acquired by the GEO600 gravitational wave detector during the S1 run. The fluctuation of the output signal-to-noise ratios was observed to be ~5% for frequencies above 1.5 kHz and at least 15% for frequencies below 1.5 kHz. The uncertainty in the estimation of the arrival time is found to be less than 0.05 s.

Calibration of the ALLEGRO resonant detector

We describe a method for calibrating the ALLEGRO resonant detector. The resulting response function can be used to transform the observed data backwards to gravitational strain data. These data are the input to a cross-correlation analysis to search for stochastic gravitational waves.