Online Pipelines¶
A number of search pipelines run in a low latency, online mode. These can be divided into two groups, modeled and unmodeled. The modeled (CBC) searches specifically look for signals from compact binary mergers of neutron stars and black holes (BNS, NSBH, and BBH systems). The unmodeled (Burst) searches on the other hand, are capable of detecting signals from a wide variety of astrophysical sources in addition to compact binary mergers: corecollapse of massive stars, magnetar starquakes, and more speculative sources such as intersecting cosmic strings or asyet unknown GW sources.
Modeled Search¶
GstLAL, MBTA, PyCBC Live and SPIIR are matchedfiltering based analysis pipelines that rapidly identify compact binary merger events, with \(\lesssim 1\) minute latencies. They use discrete banks of waveform templates to cover the target parameter space of compact binaries, with all pipelines covering the mass ranges corresponding to BNS, NSBH, and BBH systems.
A coincident analysis is performed by all pipelines, where candidate events are extracted separately from each detector via matchedfiltering and later combined across detectors. SPIIR extracts candidates from each detector via matchedfiltering and looks for coherent responses from the other detectors to provide source localization. Of the four pipelines, GstLAL and MBTA use several banks of matched filters to cover the detectors bandwidth, i.e., the templates are split across multiple frequency bands. All pipelines also implement different kinds of signalbased vetoes to reject instrumental transients that cause large SNR values but can otherwise be easily distinguished from compact binary coalescence signals.
GstLAL 1 2 is a matchedfilter pipeline designed to find gravitational waves from compact binaries in lowlatency. It uses a likelihood ratio, which increases monotonically with signal probability, to rank candidates, and then uses Monte Carlo sampling methods to estimate the distribution of likelihoodratios in noise. This distribution can then be used to compute a FAR and pvalue.
MBTA 5 constructs its background by making every possible coincidence from single detector triggers over a few hours of recent data. It then folds in the probability of a pair of triggers passing the time coincidence test.
PyCBC Live 6 7 estimates the noise background by performing timeshifted analyses using triggers from a few hours of recent data. Singledetector triggers from one detector are time shifted by every possible multiple of 100 ms, thus any resulting coincidence must be unphysical given the \(\sim 10\) ms light travel time between detectors. All such coincidences are recorded and assigned a ranking statistic. The false alarm rate is then estimated by counting accidental coincidences ranked higher than a given candidate, i.e. with a higher statistic value. When three detectors are observing at the time of a particular candidate, the most significant double coincidence is selected, and its false alarm rate is modified to take into account the data from the remaining detector.
SPIIR 3 4 applies summed parallel infinite impulse response (IIR) filters to approximate matchedfiltering results. It selects highSNR events from each detector and finds coherent responses from other detectors. It constructs a background statistical distribution by timeshifting detector data one hundred times over a week to evaluate foreground candidate significance.
Unmodeled Search¶
cWB 8 is an excess power algorithm to identify shortduration gravitational wave signals. It uses a wavelet transformation to identify timefrequency pixels that can be grouped in a single cluster if they satisfy neighboring conditions. A tuned version for compactbinary coalescences chooses the timefrequency pixels if they mainly follow a pattern that increases in frequency. A maximumlikelihoodstatistics calculated over the cluster is used to identify the proper parameter of the event, in particular the probability of the source direction and the coherent network signaltonoise ratio. The largest likelihood value is used to assign detection significance to the found events.
oLIB 9 uses the Q transform to decompose GW strain data into several timefrequency planes of constant quality factors \(Q\), where \(Q \sim \tau f_0\). The pipeline flags data segments containing excess power and searches for clusters of these segments with identical \(f_0\) and \(Q\) spaced within 100 ms of each other. Coincidences among the detector network of clusters within a 10 ms light travel time window are then analyzed with a coherent (i.e., correlated across the detector network) signal model to identify possible GW candidate events.
Note
oLIB is not currently in operation.
Coincident with External Trigger Search¶
RAVEN 10 In addition, we will operate the Rapid OnSource VOEvent Coincidence Monitor (RAVEN), a fast search for coincidences between GW and nonGW events. RAVEN will process alerts for gammaray bursts (GRBs) from both the FermiGBM instrument and the Neil Gehrels Swift Observatory, as well as galactic supernova alerts from the SNEWS collaboration. Two astronomical events are considered coincident if they are within a particular time window of each other, which varies depending on which two types of events are being considered (see the table below). Note that these time windows are centered on the GW, e.g., [1,5] s means we consider GRBs up to one second before or up to 5 seconds after the GW.
Event Type 
Time window (s) 
Notice Type Considered



CBC 
Burst 

GRB
(Fermi, Swift)

[1,5] 
[60,600] 
FERMI_GBM_ALERT
FERMI_GBM_FIN_POS
FERMI_GBM_FLT_POS
FERMI_GBM_GND_POS
FERMI_GBM_SUBTHRESH
SWIFT_BAT_GRB_ALERT
SWIFT_BAT_GRB_LC

Lowenergy Neutrinos
(SNEWS)

[10,10] 
[10,10] 
SNEWS 
In addition, RAVEN will calculate coincident FARs, one including only timing information (temporal) and one including GRB/GW sky map information (spacetime) as well. RAVEN is currently under review and is planned to be able to trigger preliminary alerts once this is finished.
LLAMA 11 12 The LowLatency Algorithm for Multimessenger Astrophysics is a an online search pipeline combining LIGO/Virgo GW triggers with High Energy Neutrino (HEN) triggers from IceCube. It finds temporallycoincident subthreshold IceCube neutrinos and performs a detailed Bayesian significance calculation to find joint GW+HEN triggers.
 1
Messick, C., Blackburn, K., Brady, P., et al. 2017, Phys. Rev. D, 95, 042001. doi:10.1103/PhysRevD.95.042001
 2
Sachdev, S., Caudill, S., Fong, H., et al. 2019. arXiv:1901.08580
 3
Hooper, S., Chung, S. K., Luan, J., et al. 2012, Phys. Rev. D, 86, 024012. doi:10.1103/PhysRevD.86.024012
 4
Chu, Q. 2017, Ph.D. Thesis, The University of Western Australia. https://api.researchrepository.uwa.edu.au/portalfiles/portal/18509751
 5
Adams, T., Buskulic, D., Germain, V., et al. 2016, Class. Quantum Grav., 33, 175012. doi:10.1088/02649381/33/17/175012
 6
Nitz, A. H., Dal Canton, T., Davis, D. & Reyes, S. 2018, Phys. Rev. D, 98, 024050. doi:10.1103/PhysRevD.98.024050
 7
Dal Canton, T., & Harry, I. W. 2017. arXiv:1705.01845
 8
Klimenko, S., Vedovato, G., Drago, M., et al. 2016, Phys. Rev. D, 93, 042004. doi:10.1103/PhysRevD.93.042004
 9
Lynch, R., Vitale, S., Essick, R., Katsavounidis, E., & Robinet, F. 2017, Phys. Rev. D, 95, 104046. doi:10.1103/PhysRevD.95.104046
 10
Urban, A. L. 2016, Ph.D. Thesis. https://dc.uwm.edu/etd/1218/
 11
Bartos, I., Veske, D., Keivani, A., et al. 2018. arXiv:1810.11467
 12
Countryman, S., Keivani, A., Bartos, I., et al. 2019. arXiv:1901.05486