20 research outputs found
Enabling high confidence detections of gravitational-wave bursts
With the advanced LIGO and Virgo detectors taking observations the detection
of gravitational waves is expected within the next few years. Extracting
astrophysical information from gravitational wave detections is a well-posed
problem and thoroughly studied when detailed models for the waveforms are
available. However, one motivation for the field of gravitational wave
astronomy is the potential for new discoveries. Recognizing and characterizing
unanticipated signals requires data analysis techniques which do not depend on
theoretical predictions for the gravitational waveform. Past searches for
short-duration un-modeled gravitational wave signals have been hampered by
transient noise artifacts, or "glitches," in the detectors. In some cases, even
high signal-to-noise simulated astrophysical signals have proven difficult to
distinguish from glitches, so that essentially any plausible signal could be
detected with at most 2-3 level confidence. We have put forth the
BayesWave algorithm to differentiate between generic gravitational wave
transients and glitches, and to provide robust waveform reconstruction and
characterization of the astrophysical signals. Here we study BayesWave's
capabilities for rejecting glitches while assigning high confidence to
detection candidates through analytic approximations to the Bayesian evidence.
Analytic results are tested with numerical experiments by adding simulated
gravitational wave transient signals to LIGO data collected between 2009 and
2010 and found to be in good agreement.Comment: 15 pages, 6 figures, submitted to PR
Neutrino tomography
Neutrinos are produced in weak interactions as states with definite flavorâelectron, muon, or tauâand these flavor states are superpositions of states of different mass. As a neutrino propagates through space, the different mass eigenstates interfere, resulting in time-dependent flavor oscillation. Though matter is transparent to neutrinos, the flavor oscillation probability is modified when neutrinos travel through matter. Herein, we present an introduction to neutrino propagation through matter in a manner accessible to advanced undergraduate students. As an interesting application, we consider neutrino propagation through matter with a piecewise-constant density profile. This scenario has relevance in neutrino tomography, in which the density profile of matter, like the Earth\u27s interior, can be probed via a broad-spectrum neutrino beam. We provide an idealized example to demonstrate the principle of neutrino tomography
Inferring the post-merger gravitational wave emission from binary neutron star coalescences
We present a robust method to characterize the gravitational wave emission
from the remnant of a neutron star coalescence. Our approach makes only minimal
assumptions about the morphology of the signal and provides a full posterior
probability distribution of the underlying waveform. We apply our method on
simulated data from a network of advanced ground-based detectors and
demonstrate the gravitational wave signal reconstruction. We study the
reconstruction quality for different binary configurations and equations of
state for the colliding neutron stars. We show how our method can be used to
constrain the yet-uncertain equation of state of neutron star matter. The
constraints on the equation of state we derive are complimentary to
measurements of the tidal deformation of the colliding neutron stars during the
late inspiral phase. In the case of a non-detection of a post-merger signal
following a binary neutron star inspiral we show that we can place upper limits
on the energy emitted.Comment: 11 pages, 12 figures, final published versio
Reconstructing gravitational wave signals from binary black hole mergers with minimal assumptions
We present a systematic comparison of the binary black hole (BBH) signal waveform reconstructed by two independent and complementary approaches used in LIGO and Virgo source inference: a template-based analysis, and a morphology-independent analysis. We apply the two approaches to real events and to two sets of simulated observations made by adding simulated BBH signals to LIGO and Virgo detector noise. The first set is representative of the 10 BBH events in the first Gravitational Wave Transient Catalog (GWTC-1). The second set is constructed from a population of BBH systems with total mass and signal strength in the ranges that ground based detectors are typically sensitive. We find that the reconstruction quality of the GWTC-1 events is consistent with the results of both sets of simulated signals. We also demonstrate a simulated case where the presence of a mismodelled effect in the observed signal, namely higher order modes, can be identified through the morphology-independent analysis. This study is relevant for currently progressing and future observational runs by LIGO and Virgo
Inferring the Post-Merger Gravitational Wave Emission from Binary Neutron Star Coalscences
We present a robust method to characterize the gravitational wave emission from the remnant of a neutron star coalescence. Our approach makes only minimal assumptions about the morphology of the signal and provides a full posterior probability distribution of the underlying waveform. We apply our method on simulated data from a network of advanced ground-based detectors and demonstrate the gravitational wave signal reconstruction. We study the reconstruction quality for different binary configurations and equations of state for the colliding neutron stars. We show how our method can be used to constrain the yet-uncertain equation of state of neutron star matter. The constraints on the equation of state we derive are complimentary to measurements of the tidal deformation of the colliding neutron stars during the late inspiral phase. In the case of a nondetection of a post-merger signal following a binary neutron star inspiral we show that we can place upper limits on the energy emitted
The BayesWave analysis pipeline in the era of gravitational wave observations
We describe updates and improvements to the BayesWave gravitational wave transient analysis pipeline, and provide examples of how the algorithm is used to analyze data from ground-based gravitational wave detectors. BayesWave models gravitational wave signals in a morphology-independent manner through a sum of frame functions, such as Morlet-Gabor wavelets or chirplets. BayesWave models the instrument noise using a combination of a parametrized Gaussian noise component and non-stationary and non-Gaussian noise transients. Both the signal model and noise model employ trans-dimensional sampling, with the complexity of the model adapting to the requirements of the data. The flexibility of the algorithm makes it suitable for a variety of analyses, including reconstructing generic unmodeled signals; cross checks against modeled analyses for compact binaries; as well as separating coherent signals from incoherent instrumental noise transients (glitches). The BayesWave model has been extended to account for gravitational wave signals with generic polarization content and the simultaneous presence of signals and glitches in the data. We describe updates in the BayesWave prior distributions, sampling proposals, and burn-in stage that provide significantly improved sampling efficiency. We present standard review checks indicating the robustness and convergence of the BayesWave trans-dimensional sampler
Enabling high confidence detections of gravitational-wave bursts
Extracting astrophysical information from gravitational-wave detections is a well-posed problem and thoroughly studied when detailed models for the waveforms are available. However, one motivation for the field of gravitational-wave astronomy is the potential for new discoveries. Recognizing and characterizing unanticipated signals requires data analysis techniques which do not depend on theoretical predictions for the gravitational waveform. Past searches for short-duration unmodeled gravitational-wave signals have been hampered by transient noise artifacts, or âglitches,â in the detectors. We have put forth the BayesWave algorithm to differentiate between generic gravitational-wave transients and glitches, and to provide robust waveform reconstruction and characterization of the astrophysical signals. Here we study BayesWaveâs capabilities for rejecting glitches while assigning high confidence to detection candidates through analytic approximations to the Bayesian evidence. Analytic results are tested with numerical experiments by adding simulated gravitational-wave transient signals to LIGO data collected between 2009 and 2010 and found to be in good agreement