Rotating neutron stars (NSs) are promising sources of gravitational waves (GWs) in the frequency band of ground-based detectors. They are expected to emit quasi-monochromatic, long-duration GW signals, called continuous waves (CWs), due to their deviations from spherical symmetry. The degree of such deformations, and hence the information about the internal structure of an NS, is encoded in a dimension-less parameter ε called ellipticity. Searches for CW signals from isolated Galactic NSs have shown to be sensitive to ellipticities as low as $\varepsilon \sim \mathcal {O}(10^{-9})$. These searches are optimal for detecting and characterizing GWs from individual NSs, but they are not designed to measure the properties of NSs as population, such as the average ellipticity εav. These ensemble properties can be determined by the measurement of the stochastic gravitational-wave background (SGWB) arising from the superposition of GW signals from individually undetectable NSs. In this work, we perform a cross-correlation search for such a SGWB using the data from the first three observation runs of Advanced LIGO and Virgo. Finding no evidence for an SGWB signal, we set upper limits on the dimension-less energy density parameter Ωgw(f). Using these results, we also constrain the average ellipticity of Galactic NSs and five NS ‘hotspots’, as a function of the number of NSs emitting GWs within the frequency band of the search Nband. We find $\varepsilon _{\mathrm{av}} \lesssim 1.8 \times 10^{-8}$, with Nband = 1.6 × 107, for Galactic NSs, and $\varepsilon _{\mathrm{av}} \lesssim [3.5-11.8]\times 10^{-7}$, with Nband = 1.6 × 1010, for NS hotspots.
Based on the prior O1–O2 observing runs, about 30% of the data collected by Advanced LIGO and Virgo in the next observing runs are expected to be single-interferometer data, i.e. they will be collected at times when only one detector in the network is operating in observing mode. Searches for gravitational-wave signals from supernova events do not rely on matched filtering techniques because of the stochastic nature of the signals. If a Galactic supernova occurs during single-interferometer times, separation of its unmodelled gravitational-wave signal from noise will be even more difficult due to lack of coherence between detectors. We present a novel machine learning method to perform single-interferometer supernova searches based on the standard LIGO-Virgo coherent WaveBurst pipeline. We show that the method may be used to discriminate Galactic gravitational-wave supernova signals from noise transients, decrease the false alarm rate of the search, and improve the supernova detection reach of the detectors.
more » « less- NSF-PAR ID:
- 10303193
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 2632-2153
- Page Range / eLocation ID:
- Article No. 015005
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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