1. Searching for gravitational-wave transients with Advanced detectors
Ik Siong Heng
on behalf of the LIGO Scientific Collaboration and Virgo
NRmGW workshop, Valencia, December 2016 G

2. Generic transient (burst) analysis
Searches for gravitational-wave bursts do not require knowledge about the phase evolution (waveform) of the expected signal
Burst searches aim to cover a broad parameter space which can overlap with well-modelled signals (eg. binary black holes)
potential for discovering new sources of gravitational waves
Steps of a typical generic burst search:
weight data by the noise at each frequency (whitening)
make time-frequency representation of the data
identify correlated excess power in multiple detectors
estimate false alarm rate of observations
Some burst searches target GWs expected from particular sources or are informed by non-GW observations of astrophysical phenomena
NRmGW workshop, Valencia, December 2016 2 G

3. Burst science All-sky search for ‘unmodelled’ transients
search for transients with long (~10-500s) and short durations
short transient searches run in low-latency (GW150914)
Search for intermediate-mass binary black holes
Search for GWs from cosmic strings kinks and cusps
Transient search associated with
GRBs
high-energy neutrinos
neutron star transients (eg. SGR flares)
nearby optical supernovae
Fast Radio Bursts
Search counterpart EM transients (follow-ups)
NRmGW workshop, Valencia, December 2016 3 G

4. Online all-sky burst searches
coherent WaveBurst (cWB)
Multi-resolution time-frequency map of data is obtained through wavelet decomposition
Triggers are analyzed coherently to estimate signal waveform, polarization, source location, using a constrained likelihood method (maximised over sky position)
Omicron+LIB (oLIB)
Data is decomposed using sine-gaussian templates to form time-frequency map; triggers within 100 ms with same central frequency and quality factor are clustered
Coincident triggers analyzed using Bayesian parameter estimation and model selection algorithm (with single sine Gaussian template)
cWB+BayesWave
Bayesian model selection using a liner combination of sine Gaussian templates (optimized using a reversible jump Markov chain Monte Carlo). Posterior distributions for estimation parameters and waveform reconstruction
NRmGW workshop, Valencia, December 2016 4

5. Online all-sky burst searches
Gravitational wave data
cWB
Omicron
cWB and oLIB generate skymaps within minutes-hours of event detection
NRmGW workshop, Valencia, December 2016
BayesWave
LIB
LIB
GraceDB
(event parameters/skymaps/annotations)
data quality 5 G

6. O1 all-sky search
Search for generic gravitational wave transients using all data from O1
No non-BBH signals were observed
Able to detect gravitational wave energy of just over 10-9M☉c2 at 10 kpc around 100 Hz
~ kpc or ~ Mpc
NRmGW workshop, Valencia, December 2016 6 G

7. O1 long-duration burst searches
Search O1 data for transients lasting between 10 and 500 seconds in duration, from 24 to 2048 Hz
Possible sources
hydrodynamic instabilities during supernovae
accretion disk instabilities (ADI) in black holes formed from collapsars (long GRBs)
magnetars deformation following GRB
Use three search algorithms
STAMP-AS, which was previously used on S5/S6 data
S5/S6 results: Phys. Rev. D 93, (2016)
cWB and X-SphRad also used for O1 data
Estimate search sensitivity improvement over S5/S6 to be about factor of 3
NRmGW workshop, Valencia, December 2016 7 G

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11. Triggered searches High-energy neutrinos Gamma-ray bursts
correlates cWB trigger times and sky location with IceCube and Antares events
Gamma-ray bursts
Search for modelled signals (short & ambiguous GRBs) and unmodelled search (all GRBs)
Also search for short and long GWs associated with magnetar flares
Fast Radio Bursts
search previously performed on FRBs detected by Green Bank Telescope and Parkes between 2007 and 2013 (arXiv: )
Supernovae (see talk by M. Szczepanczyk)
search for GWs from nearby supernovae
performed on SN2007gr and SN2011dh (arXiv: )
also have searches triggered by SNEWS alerts
ANTARES+IceCube+LIGO+Virgo PRD 2016 (arXiv: )
NRmGW workshop, Valencia, December 2016 11 G

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15. Waveform reconstruction
NRmGW workshop, Valencia, December 2016
The detected signal is fitted by a set of sine-gaussian waveforms
BayesWave uses a stochastic sampler (RJMCMC) to find likely waveforms that fit the data, leaving only gaussian noise as residual
cWB reconstructs the waveform by adding up the power of all detected time-frequency pixels 15
Phys. Rev. D 93, (2016) arxiv.org:

16. Waveform reconstruction
NRmGW workshop, Valencia, December 2016
Numerical relativity – waveform obtained from numerical relativity simulations of binary black hole coalescence using estimated signal parameters
Reconstructed (wavelet) – 90% credible region for waveforms reconstructed by combination of wavelet templates
Reconstructed (template) – 90% credible region for best fitting templates assuming binary black hole coalescence 16 G
LIGO-Virgo Collaboration, PRL 116, (2016)

17. Estimating astrophysical properties
Generic properties of the observed signal can be mapped to physical attributes of the observed system, assuming specific models (eg. GW150914)
NRmGW workshop, Valencia, December 2016 17
Phys. Rev. D 93, (2016) arxiv.org: G

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24. Points for discussion
Are there broad signal features we should be targeting?
for searches?
for astrophysical interpretation?
If a detection is made now, how should we use the current sets of simulations (waveform catalogues) to interpret our observation?
eg. use approximants to NR matter waveforms
How can we better integrate information from NR matter simulations into Burst analyses in the mid to longer term future?
in terms of designing/updating Burst analyses
in terms of generating new waveforms/catalogues
Attention: CCSN simulation and data analysis workshop with the LVC on 17th and 18th March in Pasadena
NRmGW workshop, Valencia, December 2016

25. Back up slides
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26. HEN trigger times
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