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 (:term:`CBC`) searches specifically look for signals from compact binary mergers of neutron stars and black holes (:term:`BNS`, :term:`NSBH`, and :term:`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: core-collapse of massive stars, magnetar star-quakes, and more speculative sources such as intersecting cosmic strings or as-yet unknown GW sources. Modeled Search -------------- **GstLAL**, **MBTAOnline**, **PyCBC Live** and **SPIIR** are matched-filtering based analysis pipelines that rapidly identify compact binary merger events, with sub-minute to ∼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, NS-BH, and stellar mass BBH systems. However, GstLAL and PyCBC Live and SPIIR also include intermediate-mass BBH systems and the O2 banks differ in detail from pipeline to pipeline. A coincident analysis is performed by GSTLAL, PyCBC Live, and MBTAOnline, where candidate events are extracted separately at each detector via matched-filtering and later combined across detectors. SPIIR extract candidates of each detector via matched-filtering and look for coherent responses in other detectors so that a localization of the source can be constructed. Of the four pipelines, GstLAL and MBTAOnline use several banks of matched filters to cover the detector bandwidth, i.e., the templates are split across multiple frequency bands. All pipelines also implement different kinds of signal-based vetoes to reject instrumental transients which cause large SNR values but can otherwise be easily distinguished from compact binary coalescence signals. **GSTLAL** [#GSTLAL1]_ [#GSTLAL2]_ is a matched-filter pipeline designed to find gravitational waves from compact binaries in low-latency. 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 likelihood-ratios in noise. This distribution can then be used to compute a false alarm rate and p-value. **SPIIR** [#SPIIR]_ [#SPIIRThesis]_ applies summed parallel infinite impulse response (IIR) filters to approximate matched-filtering results. It selects high-SNR events from each detector and find coherent responses from other detectors. It constructs background by time-shifting detector data one hundred times over a week to form a background statistic distribution used to evaluate foreground candidate significance. **MBTA** [#MBTA]_ 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** [#PyCBC1]_ [#PyCBC2]_ estimates the noise background by performing time-shifted analyses using triggers from a few hours of recent data. Single-detector triggers from one of the LIGO detectors are time shifted by every possible multiple of 100 ms, thus any resulting coincidence must be unphysical given the ∼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 louder than a given candidate, i.e. with a higher statistic value. Unmodeled Search ---------------- **cWB** [#cWB]_ is an excess power algorithm to identify short-duration gravitational wave-like signals. It uses a wavelet transformation to identify time-frequency pixels which can be grouped in a single cluster if they satisfy neighboring conditions. A tuned version for compact-binary coalescences chooses the time-frequency pixels if they mainly follow a pattern that increases in frequency. A maximum-likelihood-statistics 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 signal-to-noise ratio. The last one is used to assign detection significance to the found events. **oLIB** [#oLIB]_ uses the Q transform to decompose GW strain data into several time-frequency planes of constant quality factors :math:`Q`, where :math:`Q \sim \tau f_0`. The pipeline flags data segments containing excess power and searches for clusters of these segments with identical :math:`f_0` and :math:`Q` spaced within 100 ms of each other. Coincidences among the detector network of clusters with a time-of-flight window up to 10 ms are then analyzed with a coherent (i.e., correlated across the detector network) signal model to identify possible GW candidate events. Coincident with External Trigger Search --------------------------------------- **RAVEN** [#RAVEN]_ In addition, we will operate the Rapid On-Source VOEvent Coincidence Monitor (RAVEN), a fast search for coincidences between GW online pipeline events and gamma-ray bursts or galactic supernova notifications. A similar approach is under development for high energy neutrinos. .. |apj| replace:: *Astrophys. J.* .. |cqg| replace:: *Class. Quantum Grav.* .. |prd| replace:: *Phys. Rev. D* .. [#GSTLAL1] Messick, C., Blackburn, K., Brady, P., et al. 2017, |prd|, 95, 042001. https://doi.org/10.1103/PhysRevD.95.042001 .. [#GSTLAL2] Sachdev, S., Caudill, S., Fong, H., et al. 2019, arXiv, 1901.08580. https://arxiv.org/abs/1901.08580 .. [#SPIIR] Hooper, S., Chung, S. K., Luan, J., et al. 2012, |prd|, 86, 024012. https://doi.org/10.1103/PhysRevD.86.024012 .. [#SPIIRThesis] Chu, Q. 2017, Ph.D. Thesis. https://api.research-repository.uwa.edu.au/portalfiles/portal/18509751 .. [#MBTA] Adams, T., Buskulic, D., Germain, V., et al. 2016, |cqg|, 33, 175012. http://doi.org/10.1088/0264-9381/33/17/175012 .. [#PyCBC1] Nitz, A. H., Dent, T., Dal Canton, T., Fairhurst, S., & Brown, D. A. 2017, |apj|, 849, 118. https://doi.org/10.3847/1538-4357/aa8f50 .. [#PyCBC2] Dal Canton, T., & Harry, I. W. 2017. https://arxiv.org/abs/1705.01845 .. [#cWB] Klimenko, S., Vedovato, G., Drago, M., et al. 2016, |prd|, 93, 042004. https://doi.org/10.1103/PhysRevD.93.042004 .. [#oLIB] Lynch, R., Vitale, S., Essick, R., Katsavounidis, E., & Robinet, F. 2017, |prd|, 95, 104046. https://doi.org/10.1103/PhysRevD.95.104046 .. [#RAVEN] Urban, A. L. 2016, Ph.D. Thesis. http://adsabs.harvard.edu/abs/2016PhDT.........8U