Search

On April 1st, 2019, the Advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO), joined by the Advanced Virgo detector, began the third observing run, a year-long dedicated search for gravitational radiation. The LIGO detectors have achieved a higher duty cycle and greater sensitivity to gravitational waves than ever before, with LIGO Hanford achieving angle-averaged sensitivity to binary neutron star coalescences to a distance of 111 Mpc, and LIGO Livingston to 134 Mpc with duty factors of 74.6% and 77.0% respectively. The improvement in sensitivity and stability is a result of several upgrades to the detectors, including doubled intracavity power, the addition of an in-vacuum optical parametric oscillator for squeezed-light injection, replacement of core optics and end reaction masses, and installation of acoustic mode dampers. This paper explores the purposes behind these upgrades, and explains to the best of our knowledge the noise currently limiting the sensitivity of each detector. [ABSTRACT FROM AUTHOR]

The worldwide advanced gravitational-wave (GW) detector network has so far primarily consisted of the two Advanced LIGO observatories at Hanford and Livingston, with Advanced Virgo joining the 2016-7 O2 observation run at a relatively late stage. However Virgo has been observing alongside the LIGO detectors since the start of the O3 run; in the near future, the KAGRA detector will join the global network and a further LIGO detector in India is under construction. Gravitational-wave search methods would therefore benefit from the ability to analyse data from an arbitrary network of detectors. In this paper, we extend the PyCBC offline compact binary coalescence (CBC) search analysis to three or more detectors, and describe resulting updates to the coincident search and event ranking statistic. For a three-detector network, our improved multi-detector search finds 23% more simulated signals at fixed false alarm rate in idealized colored Gaussian noise, and up to 40% more in real data, compared to the two-detector analysis previously used during O2. [ABSTRACT FROM AUTHOR]

A first detection of terrestrial gravity noise in gravitational-wave detectors is a formidable challenge. With the help of environmental sensors, it can in principle be achieved before the noise becomes dominant by estimating correlations between environmental sensors and the detector. The main complication is to disentangle different coupling mechanisms between the environment and the detector. In this paper, we analyze the relations between physical couplings and correlations that involve ground motion and LIGO strain data h(t) recorded during its second science run in 2016 and 2017. We find that all noise correlated with ground motion was more than an order of magnitude lower than dominant low-frequency instrument noise, and the dominant coupling over part of the spectrum between ground and h(t) was residual coupling through the seismic-isolation system. We also present the most accurate gravitational coupling model so far based on a detailed analysis of data from a seismic array. Despite our best efforts, we were not able to unambiguously identify gravitational coupling in the data, but our improved models confirm previous predictions that gravitational coupling might already dominate linear ground-to-h(t) coupling over parts of the low-frequency, gravitational-wave observation band. [ABSTRACT FROM AUTHOR]

Deep learning algorithms, in particular neural networks, have been steadily gaining popularity among the gravitational wave community for the last few years. The reliability and accuracy of deep learning approaches in gravitational wave detection, parameter estimation, and glitch classification have already been proved and verified by several groups in recent years. In this paper, we report on the construction of the deep artificial neural network (ANN) to localize simulated gravitational wave signals in the sky with high accuracy. We have modeled the sky as a sphere and have considered cases in which the sphere is divided into 18, 50, 128, 1024, 2048, and 4096 sectors. The sky direction of the gravitational wave source is estimated by classifying the signal into one of these sectors based on its right ascension and declination values for each of these cases. To do this, we have injected simulated binary black hole gravitational wave signals of component masses sampled uniformly between 30 M⊙ and 80 M⊙ into Gaussian noise and used the whitened strain values to obtain the input features for training our ANN. We input features such as the delays in arrival times, phase differences, and amplitude ratios at each of the three detectors Hanford, Livingston, and Virgo, from the raw time-domain strain values as well as from analytical versions of these signals, obtained through Hilbert transformation. We show that our model is able to classify gravitational wave samples, not used in the training process, into their correct sectors with very high accuracy (greater than 90%) for coarse angular resolution using 18, 50, and 128 sectors. We also test our localization on test samples with injection parameters of the published LIGO binary black hole merger events GW150914, GW170818, and GW170823 for 1024, 2048, and 4096 sectors and compare the result with that from BAYESTAR and parameter estimation. In addition, we report that the time taken by our model to localize one gravitational wave signal is around 0.018 s on 14 Intel Xeon CPU cores. [ABSTRACT FROM AUTHOR]