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Register a new userFirst low frequency all-sky search for continuous gravitational wave signals
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In this paper we present the results of the first low frequency all-sky search of continuous gravitational wave signals conducted on Virgo VSR2 and VSR4 data. The search covered the full sky, a frequency range between 20 Hz and 128 Hz with a range of spin-down between $-1.0 times 10^{-10}$ Hz/s and $+1.5 times 10^{-11}$ Hz/s, and was based on a hierarchical approach. The starting point was a set of short Fast Fourier Transforms (FFT), of length 8192 seconds, built from the calibrated strain data. Aggressive data cleaning, both in the time and frequency domains, has been done in order to remove, as much as possible, the effect of disturbances of instrumental origin. On each dataset a number of candidates has been selected, using the FrequencyHough transform in an incoherent step. Only coincident candidates among VSR2 and VSR4 have been examined in order to strongly reduce the false alarm probability, and the most significant candidates have been selected. Selected candidates have been subject to a follow-up by constructing a new set of longer FFTs followed by a further incoherent analysis, still based on the FrequencyHough transform. No evidence for continuous gravitational wave signals was found, therefore we have set a population-based joint VSR2-VSR4 90$%$ confidence level upper limit on the dimensionless gravitational wave strain in the frequency range between 20 Hz and 128 Hz. This is the first all-sky search for continuous gravitational waves conducted, on data of ground-based interferometric detectors, at frequencies below 50 Hz. We set upper limits in the range between about $10^{-24}$ and $2times 10^{-23}$ at most frequencies. Our upper limits on signal strain show an improvement of up to a factor of $sim$2 with respect to the results of previous all-sky searches at frequencies below $80~mathrm{Hz}$.
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In this paper we present the results of a coherent narrow-band search for continuous gravitational-wave signals from the Crab and Vela pulsars conducted on Virgo VSR4 data. In order to take into account a possible small mismatch between the gravitational wave frequency and two times the star rotation frequency, inferred from measurement of the electromagnetic pulse rate, a range of 0.02 Hz around two times the star rotational frequency has been searched for both the pulsars. No evidence for a signal has been found and 95$%$ confidence level upper limits have been computed both assuming polarization parameters are completely unknown and that they are known with some uncertainty, as derived from X-ray observations of the pulsar wind torii. For Vela the upper limits are comparable to the spin-down limit, computed assuming that all the observed spin-down is due to the emission of gravitational waves. For Crab the upper limits are about a factor of two below the spin-down limit, and represent a significant improvement with respect to past analysis. This is the first time the spin-down limit is significantly overcome in a narrow-band search.
Gravitational-wave radiometry is a powerful tool by which weak signals with unknown signal morphologies are recovered through a process of cross correlation. Radiometry has been used, e.g., to search for persistent signals from known neutron stars such as Scorpius X-1. In this paper, we demonstrate how a more ambitious search--for persistent signals from unknown neutron stars--can be efficiently carried out using folded data, in which an entire ~year-long observing run is represented as a single sidereal day. The all-sky, narrowband radiometer search described here will provide a computationally tractable means to uncover gravitational-wave signals from unknown, nearby neutron stars in binary systems, which can have modulation depths of ~0.1-2 Hz. It will simultaneously provide a sensitive search algorithm for other persistent, narrowband signals from unexpected sources.
Detecting continuous nanohertz gravitational waves (GWs) generated by individual close binaries of supermassive black holes (CB-SMBHs) is one of the primary objectives of pulsar timing arrays (PTAs). The detection sensitivity is slated to increase significantly as the number of well-timed millisecond pulsars will increase by more than an order of magnitude with the advent of next-generation radio telescopes. Currently, the Bayesian analysis pipeline using parallel tempering Markov chain Monte Carlo has been applied in multiple studies for CB-SMBH searches, but it may be challenged by the high dimensionality of the parameter space for future large-scale PTAs. One solution is to reduce the dimensionality by maximizing or marginalizing over uninformative parameters semi-analytically, but it is not clear whether this approach can be extended to more complex signal models without making overly simplified assumptions. Recently, the method of diffusive nested (DNest) sampling shown the capability of coping with high dimensionality and multimodality effectively in Bayesian analysis. In this paper, we apply DNest to search for continuous GWs in simulated pulsar timing residuals and find that it performs well in terms of accuracy, robustness, and efficiency for a PTA including $mathcal{O}(10^2)$ pulsars. DNest also allows a simultaneous search of multiple sources elegantly, which demonstrates its scalability and general applicability. Our results show that it is convenient and also high beneficial to include DNest in current toolboxes of PTA analysis.
We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.
We report results of a deep all-sky search for periodic gravitational waves from isolated neutron stars in data from the first Advanced LIGO observing run. This search investigates the low frequency range of Advanced LIGO data, between 20 and 100 Hz, much of which was not explored in initial LIGO. The search was made possible by the computing power provided by the volunteers of the Einstein@Home project. We find no significant signal candidate and set the most stringent upper limits to date on the amplitude of gravitational wave signals from the target population, corresponding to a sensitivity depth of 48.7 [1/$sqrt{{textrm{Hz}}}$]. At the frequency of best strain sensitivity, near 100 Hz, we set 90% confidence upper limits of $1.8 times 10^{-25}$. At the low end of our frequency range, 20 Hz, we achieve upper limits of $3.9 times 10^{-24}$. At 55 Hz we can exclude sources with ellipticities greater than $10^{-5}$ within 100 pc of Earth with fiducial value of the principal moment of inertia of $10^{38} textrm{kg m}^2$.
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