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Register a new userObserving Gravitational Waves with a Single Detector
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A major challenge of any search for gravitational waves is to distinguish true astrophysical signals from those of terrestrial origin. Gravitational-wave experiments therefore make use of multiple detectors, considering only those signals which appear in coincidence in two or more instruments. It is unclear, however, how to interpret loud gravitational-wave candidates observed when only one detector is operational. In this paper, we demonstrate that the observed rate of binary black hole mergers can be leveraged in order to make confident detections of gravitational-wave signals with one detector alone. We quantify detection confidences in terms of the probability $P(S)$ that a signal candidate is of astrophysical origin. We find that, at current levels of instrumental sensitivity, loud signal candidates observed with a single Advanced LIGO detector can be assigned $P(S)gtrsim0.4$. In the future, Advanced LIGO may be able to observe single-detector events with confidences exceeding $P(S)sim90%$.
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The next galactic core-collapse supernova (CCSN) has already exploded, and its electromagnetic (EM) waves, neutrinos, and gravitational waves (GWs) may arrive at any moment. We present an extensive study on the potential sensitivity of prospective detection scenarios for GWs from CCSNe within 5Mpc, using realistic noise at the predicted sensitivity of the Advanced LIGO and Advanced Virgo detectors for 2015, 2017, and 2019. We quantify the detectability of GWs from CCSNe within the Milky Way and Large Magellanic Cloud, for which there will be an observed neutrino burst. We also consider extreme GW emission scenarios for more distant CCSNe with an associated EM signature. We find that a three detector network at design sensitivity will be able to detect neutrino-driven CCSN explosions out to ~5.5 kpc, while rapidly rotating core collapse will be detectable out to the Large Magellanic Cloud at 50kpc. Of the phenomenological models for extreme GW emission scenarios considered in this study, such as long-lived bar-mode instabilities and disk fragmentation instabilities, all models considered will be detectable out to M31 at 0.77 Mpc, while the most extreme models will be detectable out to M82 at 3.52 Mpc and beyond.
We describe a multivariate classifier for candidate events in a templated search for gravitational-wave (GW) inspiral signals from neutron-star--black-hole (NS-BH) binaries, in data from ground-based detectors where sensitivity is limited by non-Gaussian noise transients. The standard signal-to-noise ratio (SNR) and chi-squared test for inspiral searches use only properties of a single matched filter at the time of an event; instead, we propose a classifier using features derived from a bank of inspiral templates around the time of each event, and also from a search using approximate sine-Gaussian templates. The classifier thus extracts additional information from strain data to discriminate inspiral signals from noise transients. We evaluate a Random Forest classifier on a set of single-detector events obtained from realistic simulated advanced LIGO data, using simulated NS-BH signals added to the data. The new classifier detects a factor of 1.5 -- 2 more signals at low false positive rates as compared to the standard re-weighted SNR statistic, and does not require the chi-squared test to be computed. Conversely, if only the SNR and chi-squared values of single-detector events are available, Random Forest classification performs nearly identically to the re-weighted SNR.
The characterization of the Advanced LIGO detectors in the second and third observing runs has increased the sensitivity of the instruments, allowing for a higher number of detectable gravitational-wave signals, and provided confirmation of all observed gravitational-wave events. In this work, we present the methods used to characterize the LIGO detectors and curate the publicly available datasets, including the LIGO strain data and data quality products. We describe the essential role of these datasets in LIGO-Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources and include details on the provenance of these datasets in order to support analyses of LIGO data by the broader community. Finally, we explain anticipated changes in the role of detector characterization and current efforts to prepare for the high rate of gravitational-wave alerts and events in future observing runs.
Gravitational wave searches for continuous-wave signals from neutron stars are especially challenging when the stars spin frequency is unknown a priori from electromagnetic observations and wanders stochastically under the action of internal (e.g. superfluid or magnetospheric) or external (e.g. accretion) torques. It is shown that frequency tracking by hidden Markov model (HMM) methods can be combined with existing maximum likelihood coherent matched filters like the F-statistic to surmount some of the challenges raised by spin wandering. Specifically it is found that, for an isolated, biaxial rotor whose spin frequency walks randomly, HMM tracking of the F-statistic output from coherent segments with duration T_drift = 10d over a total observation time of T_obs = 1yr can detect signals with wave strains h0 > 2e-26 at a noise level characteristic of the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO). For a biaxial rotor with randomly walking spin in a binary orbit, whose orbital period and semi-major axis are known approximately from electromagnetic observations, HMM tracking of the Bessel-weighted F-statistic output can detect signals with h0 > 8e-26. An efficient, recursive, HMM solver based on the Viterbi algorithm is demonstrated, which requires ~10^3 CPU-hours for a typical, broadband (0.5-kHz) search for the low-mass X-ray binary Scorpius X-1, including generation of the relevant F-statistic input. In a realistic observational scenario, Viterbi tracking successfully detects 41 out of 50 synthetic signals without spin wandering in Stage I of the Scorpius X-1 Mock Data Challenge convened by the LIGO Scientific Collaboration down to a wave strain of h0 = 1.1e-25, recovering the frequency with a root-mean-square accuracy of <= 4.3e-3 Hz.
We present an effective, low-dimensionality frequency-domain template for the gravitational wave signal from the stellar remnants from binary neutron star coalescence. A principal component decomposition of a suite of numerical simulations of binary neutron star mergers is used to construct orthogonal basis functions for the amplitude and phase spectra of the waveforms for a variety of neutron star equations of state and binary mass configurations. We review the phenomenology of late merger / post-merger gravitational wave emission in binary neutron star coalescence and demonstrate how an understanding of the dynamics during and after the merger leads to the construction of a universal spectrum. We also provide a discussion of the prospects for detecting the post-merger signal in future gravitational wave detectors as a potential contribution to the science case for third generation instruments. The template derived in our analysis achieves $>90%$ match across a wide variety of merger waveforms and strain sensitivity spectra for current and potential gravitational wave detectors. A Fisher matrix analysis yields a preliminary estimate of the typical uncertainty in the determination of the dominant post-merger oscillation frequency $f_{mathrm{peak}}$ as $delta f_{mathrm{peak}} sim 50$Hz. Using recently derived correlations between $f_{mathrm{peak}}$ and the neutron star radii, this suggests potential constraints on the radius of a fiducial neutron star of $sim 220$,m. Such measurements would only be possible for nearby ($sim 30$Mpc) sources with advanced LIGO but become more feasible for planned upgrades to advanced LIGO and other future instruments, leading to constraints on the high density neutron star equation of state which are independent and complementary to those inferred from the pre-merger inspiral gravitational wave signal.
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