A robust machine learning algorithm to search for continuous gravitational waves

Many continuous gravitational wave searches are affected by instrumental spectral lines that could be confused with a continuous astrophysical signal. Several techniques have been developed to limit the effect of these lines by penalising signals that appear in only a single detector. We have developed a general method, using a convolutional neural network, to reduce the impact of instrumental artefacts on searches that use the SOAP algorithm. The method can identify features in corresponding frequency bands of each detector and classify these bands as containing a signal, an instrumental line, or noise. We tested the method against four different data-sets: Gaussian noise with time gaps, data from the final run of Initial LIGO (S6) with signals added, the reference S6 mock data challenge data set and signals injected into data from the second advanced LIGO observing run (O2). Using the S6 mock data challenge data set and at a 1% false alarm probability we showed that at 95% efficiency a fully-automated SOAP search has a sensitivity corresponding to a coherent signal-to-noise ratio of 110, equivalent to a sensitivity depth of 10 Hz$^{-1/2}$, making this automated search competitive with other searches requiring significantly more computing resources and human intervention.

First search for long-duration transient gravitational waves after glitches in the Vela and Crab pulsars

Gravitational waves (GWs) can offer a novel window into the structure and dynamics of neutron stars. Here we present the first search for long-duration quasi-monochromatic GW transients triggered by pulsar glitches. We focus on two glitches observed in radio timing of the Vela pulsar (PSR J0835-4510) on 12 December 2016 and the Crab pulsar (PSR J0534+2200) on 27 March 2017, during the Advanced LIGO second observing run (O2). We assume the GW frequency lies within a narrow band around twice the spin frequency as known from radio observatons. Using the fully-coherent transient-enabled F-statistic method to search for transients of up to four months in length. We find no credible GW candidates for either target, and through simulated signal injections we set 90% upper limits on (constant) GW strain as a function of transient duration. For the larger Vela glitch, we come close to beating an indirect upper limit for when the total energy liberated in the glitch would be emitted as GWs, thus demonstrating that similar post-glitch searches at improved detector sensitivity can soon yield physical constraints on glitch models.

A targeted spectral interpolation algorithm for the detection of continuous gravitational waves

We present an improved method of targeting continuous gravitational-wave signals in data from the LIGO and Virgo detectors with a higher efficiency than the time-domain Bayesian pipeline used in many previous searches. Our spectral interpolation algorithm, SplInter, removes the intrinsic phase evolution of the signal from source rotation and relative detector motion. We do this in the frequency domain and generate a time series containing only variations in the signal due to the antenna pattern. Although less flexible than the classic heterodyne approach, SplInter allows for rapid analysis of putative signals from isolated (and some binary) pulsars, and efficient follow-up searches for candidate signals generated by other search methods. The computational saving over the heterodyne approach can be many orders of magnitude, up to a factor of around fifty thousand in some cases, with a minimal impact on overall sensitivity for most targets.