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Register a new userNovel directed search strategy to detect continuous gravitational waves from neutron stars in low- and high-eccentricity binary systems
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We describe a novel, very fast and robust, directed search incoherent method for periodic gravitational waves (GWs) from neutron stars in binary systems. As directed search, we assume the source sky position to be known with enough accuracy, but all other parameters are supposed to be unknown. We exploit the frequency-modulation due to source orbital motion to unveil the signal signature by commencing from a collection of time and frequency peaks. We validate our pipeline adding 131 artificial continuous GW signals from pulsars in binary systems to simulated detector Gaussian noise, characterized by a power spectral density Sh = 4x10^-24 Hz^-1/2 in the frequency interval [70, 200] Hz, which is overall commensurate with the advanced detector design sensitivities. The pipeline detected 128 signals, and the weakest signal injected and detected has a GW strain amplitude of ~10^-24, assuming one month of gapless data collected by a single advanced detector. We also provide sensitivity estimations, which show that, for a single- detector data covering one month of observation time, depending on the source orbital Doppler modulation, we can detect signals with an amplitude of ~7x10^-25. By using three detectors, and one year of data, we would easily gain more than a factor 3 in sensitivity, translating into being able to detect weaker signals. We also discuss the parameter estimate proficiency of our method, as well as computational budget, which is extremely cheap. In fact, sifting one month of single-detector data and 131 Hz-wide frequency range takes roughly 2.4 CPU hours. Due to the high computational speed, the current procedure can be readily applied in ally-sky schemes, sieving in parallel as many sky positions as permitted by the available computational power.
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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.
We describe a directed search for continuous gravitational waves in data from the sixth LIGO science run. The target was the nearby globular cluster NGC 6544 at a distance of 2.7 kpc. The search covered a broad band of frequencies along with first and second frequency derivatives for a fixed sky position. The search coherently integrated data from the two LIGO interferometers over a time span of 9.2 days using the matched-filtering F-statistic. We found no gravitational-wave signals and set 95% confidence upper limits as stringent as 6.0 X 10^{-25} on intrinsic strain and 8.5 X 10^{-6} on fiducial ellipticity. These values beat the indirect limits from energy conservation for stars with characteristic spindown ages older than 300 years and are within the range of theoretical predictions for possible neutron-star ellipticities. An important feature of this search was use of a barycentric resampling algorithm which substantially reduced computational cost; this method will be used extensively in searches of Advanced LIGO and Virgo detector data.
We present the results of a directed search for continuous gravitational waves from unknown, isolated neutron stars in the Galactic Center region, performed on two years of data from LIGOs fifth science run from two LIGO detectors. The search uses a semi-coherent approach, analyzing coherently 630 segments, each spanning 11.5 hours, and then incoherently combining the results of the single segments. It covers gravitational wave frequencies in a range from 78 to 496 Hz and a frequency-dependent range of first order spindown values down to -7.86 x 10^-8 Hz/s at the highest frequency. No gravitational waves were detected. We place 90% confidence upper limits on the gravitational wave amplitude of sources at the Galactic Center. Placing 90% confidence upper limits on the gravitational wave amplitude of sources at the Galactic Center, we reach ~3.35x10^-25 for frequencies near 150 Hz. These upper limits are the most constraining to date for a large-parameter-space search for continuous gravitational wave signals.
We present the first results of an all-sky search for continuous gravitational waves from unknown spinning neutron stars in binary systems using LIGO and Virgo data. Using a specially developed analysis program, the TwoSpect algorithm, the search was carried out on data from the sixth LIGO Science Run and the second and third Virgo Science Runs. The search covers a range of frequencies from 20 Hz to 520 Hz, a range of orbital periods from 2 to ~2,254 h and a frequency- and period-dependent range of frequency modulation depths from 0.277 to 100 mHz. This corresponds to a range of projected semi-major axes of the orbit from ~0.6e-3 ls to ~6,500 ls assuming the orbit of the binary is circular. While no plausible candidate gravitational wave events survive the pipeline, upper limits are set on the analyzed data. The most sensitive 95% confidence upper limit obtained on gravitational wave strain is 2.3e-24 at 217 Hz, assuming the source waves are circularly polarized. Although this search has been optimized for circular binary orbits, the upper limits obtained remain valid for orbital eccentricities as large as 0.9. In addition, upper limits are placed on continuous gravitational wave emission from the low-mass x-ray binary Scorpius X-1 between 20 Hz and 57.25 Hz.
The groundbreaking discoveries of gravitational waves from binary black-hole mergers and, most recently, coalescing neutron stars started a new era of Multi-Messenger Astrophysics and revolutionized our understanding of the Cosmos. Machine learning techniques such as artificial neural networks are already transforming many technological fields and have also proven successful in gravitational-wave astrophysics for detection and characterization of gravitational-wave signals from binary black holes. Here we use a deep-learning approach to rapidly identify transient gravitational-wave signals from binary neutron star mergers in noisy time series representative of typical gravitational-wave detector data. Specifically, we show that a deep convolution neural network trained on 100,000 data samples can rapidly identify binary neutron star gravitational-wave signals and distinguish them from noise and signals from merging black hole binaries. These results demonstrate the potential of artificial neural networks for real-time detection of gravitational-wave signals from binary neutron star mergers, which is critical for a prompt follow-up and detailed observation of the electromagnetic and astro-particle counterparts accompanying these important transients.
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