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Register a new userA method to search for long duration gravitational wave transients from isolated neutron stars using the generalized FrequencyHough
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We describe a method to detect gravitational waves lasting $O(hours-days)$ emitted by young, isolated neutron stars, such as those that could form after a supernova or a binary neutron star merger, using advanced LIGO/Virgo data. The method is based on a generalization of the FrequencyHough (FH), a pipeline that performs hierarchical searches for continuous gravitational waves by mapping points in the time/frequency plane of the detector to lines in the frequency/spindown plane of the source. We show that signals whose spindowns are related to their frequencies by a power law can be transformed to coordinates where the behavior of these signals is always linear, and can therefore be searched for by the FH. We estimate the sensitivity of our search across different braking indices, and describe the portion of the parameter space we could explore in a search using varying fast Fourier Transform (FFT) lengths.
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We present a comprehensive study of the effectiveness of Convolution Neural Networks (CNNs) to detect long duration transient gravitational-wave signals lasting $O(hours-days)$ from isolated neutron stars. We determine that CNNs are robust towards signal morphologies that differ from the training set, and they do not require many training injections/data to guarantee good detection efficiency and low false alarm probability. In fact, we only need to train one CNN on signal/noise maps in a single 150 Hz band; afterwards, the CNN can distinguish signals/noise well in any band, though with different efficiencies and false alarm probabilities due to the non-stationary noise in LIGO/Virgo. We demonstrate that we can control the false alarm probability for the CNNs by selecting the optimal threshold on the outputs of the CNN, which appears to be frequency dependent. Finally we compare the detection efficiencies of the networks to a well-established algorithm, the Generalized FrequencyHough (GFH), which maps curves in the time/frequency plane to lines in a plane that relates to the initial frequency/spindown of the source. The networks have similar sensitivities to the GFH but are orders of magnitude faster to run and can detect signals to which the GFH is blind. Using the results of our analysis, we propose strategies to apply CNNs to a real search using LIGO/Virgo data to overcome the obstacles that we would encounter, such as a finite amount of training data. We then use our networks and strategies to run a real search for a remnant of GW170817, making this the first time ever that a machine learning method has been applied to search for a gravitational wave signal from an isolated neutron star.
As the sensitivity and observing time of gravitational-wave detectors increase, a more diverse range of signals is expected to be observed from a variety of sources. Especially, long-lived gravitational-wave transients have received interest in the last decade. Because most of long-duration signals are poorly modeled, detection must rely on generic search algorithms, which make few or no assumption on the nature of the signal. However, the computational cost of those searches remains a limiting factor, which leads to sub-optimal sensitivity. Several detection algorithms have been developed to cope with this issue. In this paper, we present a new data analysis pipeline to search for un-modeled long-lived transient gravitational-wave signals with duration between 10 and 1000 s, based on an excess cross-power statistic in a network of detectors. The pipeline implements several new features that are intended to reduce computational cost and increase detection sensitivity for a wide range of signal morphologies. The method is generalized to a network of an arbitrary number of detectors and aims to provide a stable interface for further improvements. Comparisons with a previous implementation of a similar method on simulated and real gravitational-wave data show an overall increase in detection efficiency depending on the signal morphology, and a computing time reduced by at least a factor 10.
We present the results of a search for long-duration gravitational wave transients in two sets of data collected by the LIGO Hanford and LIGO Livingston detectors between November 5, 2005 and September 30, 2007, and July 7, 2009 and October 20, 2010, with a total observational time of 283.0 days and 132.9 days, respectively. The search targets gravitational wave transients of duration 10 - 500 s in a frequency band of 40 - 1000 Hz, with minimal assumptions about the signal waveform, polarization, source direction, or time of occurrence. All candidate triggers were consistent with the expected background; as a result we set 90% confidence upper limits on the rate of long-duration gravitational wave transients for different types of gravitational wave signals. For signals from black hole accretion disk instabilities, we set upper limits on the source rate density between $3.4 times 10^{-5}$ - $9.4 times 10^{-4}$ Mpc$^{-3}$ yr$^{-1}$ at 90% confidence. These are the first results from an all-sky search for unmodeled long-duration transient gravitational waves.
We present an algorithm for the identification of transient noise artifacts (glitches) in cross-correlation searches for long O(10s) gravitational-wave transients. The algorithm utilizes the auto-power in each detector as a discriminator between well-behaved Gaussian noise (possibly including a gravitational-wave signal) and glitches. We test the algorithm with both Monte Carlo noise and time-shifted data from the LIGO S5 science run and find that it is effective at removing a significant fraction of glitches while keeping the vast majority (99.6%) of the data. Using an accretion disk instability signal model, we estimate that the algorithm is accidentally triggered at a rate of less than 10^-5% by realistic signals, and less than 3% even for exceptionally loud signals. We conclude that the algorithm is a safe and effective method for cleaning the cross-correlation data used in searches for long gravitational-wave transients.
We present the results of a search for long-duration gravitational-wave transients in the data from the Advanced LIGO second observation run; we search for gravitational-wave transients of $2~text{--}~ 500$~s duration in the $24 - 2048$,Hz frequency band with minimal assumptions about signal properties such as waveform morphologies, polarization, sky location or time of occurrence. Targeted signal models include fallback accretion onto neutron stars, broadband chirps from innermost stable circular orbit waves around rotating black holes, eccentric inspiral-merger-ringdown compact binary coalescence waveforms, and other models. The second observation run totals about otwoduration~days of coincident data between November 2016 and August 2017. We find no significant events within the parameter space that we searched, apart from the already-reported binary neutron star merger GW170817. We thus report sensitivity limits on the root-sum-square strain amplitude $h_{mathrm{rss}}$ at $50%$ efficiency. These sensitivity estimates are an improvement relative to the first observing run and also done with an enlarged set of gravitational-wave transient waveforms. Overall, the best search sensitivity is $h_{mathrm{rss}}^{50%}$=$2.7times10^{-22}$~$mathrm{Hz^{-1/2}}$ for a millisecond magnetar model. For eccentric compact binary coalescence signals, the search sensitivity reaches $h_{mathrm{rss}}^{50%}$=$9.6times10^{-22}$~$mathrm{Hz^{-1/2}}$.
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