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**, **MBTA**, **PyCBC Live** and **SPIIR** are matched-filtering based analysis pipelines that rapidly identify compact binary merger events, with :math:`\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 :term:`BNS`, :term:`NSBH`, and :term:`BBH` systems. A coincident analysis is performed by all pipelines, where candidate events are extracted separately from each detector via matched-filtering and later combined across detectors. SPIIR extracts candidates from each detector via matched-filtering 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 signal-based vetoes to reject instrumental transients that cause large :term:`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 :term:`FAR` and p-value. **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 detector are time shifted by every possible multiple of 100 ms, thus any resulting coincidence must be unphysical given the :math:`\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** [#SPIIR]_ [#SPIIRThesis]_ applies summed parallel infinite impulse response (IIR) filters to approximate matched-filtering results. It selects high-:term:`SNR` events from each detector and finds coherent responses from other detectors. It constructs a background statistical distribution by time-shifting detector data one hundred times over a week to evaluate foreground candidate significance. Unmodeled Search ---------------- **cWB** [#cWB]_ is an excess power algorithm to identify short-duration gravitational wave signals. It uses a wavelet transformation to identify time-frequency pixels that 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 largest likelihood value 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 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** [#RAVEN]_ In addition, we will operate the Rapid On-Source VOEvent Coincidence Monitor (RAVEN), a fast search for coincidences between GW and non-GW events. RAVEN will process alerts for gamma-ray bursts (GRBs) from both the *Fermi*-GBM 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 | | | | | (`see full list`_) | | +-----------+-----------+ | | | CBC | Burst | .. _see full list: http://gcn.gsfc.nasa.gov/filtering.html | +=======================+===========+===========+=================================================================+ | | GRB | [-1,5] | [-60,600] | | FERMI_GBM_ALERT | | | (*Fermi*, *Swift*) | | | | FERMI_GBM_FIN_POS | | | | | | FERMI_GBM_FLT_POS | | | | | | FERMI_GBM_GND_POS | | | | | | FERMI_GBM_SUBTHRESH | | | | | | SWIFT_BAT_GRB_ALERT | | | | | | SWIFT_BAT_GRB_LC | +-----------------------+-----------+-----------+-----------------------------------------------------------------+ | | Low-energy Neutrinos| [-10,10] | [-10,10] | SNEWS | | | (SNEWS) | | | | +-----------------------+-----------+-----------+-----------------------------------------------------------------+ In addition, RAVEN will calculate coincident :term:`FARs `, one including only timing information (temporal) and one including GRB/GW sky map information (space-time) as well. RAVEN is currently under review and is planned to be able to trigger preliminary alerts once this is finished. **LLAMA** [#LLAMA1]_ [#LLAMA2]_ The `Low-Latency Algorithm for Multi-messenger Astrophysics `__ is a an online search pipeline combining LIGO/Virgo GW triggers with High Energy Neutrino (HEN) triggers from IceCube. It finds temporally-coincident sub-threshold IceCube neutrinos and performs a detailed Bayesian significance calculation to find joint GW+HEN triggers. .. |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. :doi:`10.1103/PhysRevD.95.042001` .. [#GstLAL2] Sachdev, S., Caudill, S., Fong, H., et al. 2019. :arxiv:`1901.08580` .. [#SPIIR] Hooper, S., Chung, S. K., Luan, J., et al. 2012, |prd|, 86, 024012. :doi:`10.1103/PhysRevD.86.024012` .. [#SPIIRThesis] Chu, Q. 2017, Ph.D. Thesis, The University of Western Australia. https://api.research-repository.uwa.edu.au/portalfiles/portal/18509751 .. [#MBTA] Adams, T., Buskulic, D., Germain, V., et al. 2016, |cqg|, 33, 175012. :doi:`10.1088/0264-9381/33/17/175012` .. [#PyCBC1] Nitz, A. H., Dal Canton, T., Davis, D. & Reyes, S. 2018, |prd|, 98, 024050. :doi:`10.1103/PhysRevD.98.024050` .. [#PyCBC2] Dal Canton, T., & Harry, I. W. 2017. :arxiv:`1705.01845` .. [#cWB] Klimenko, S., Vedovato, G., Drago, M., et al. 2016, |prd|, 93, 042004. :doi:`10.1103/PhysRevD.93.042004` .. [#oLIB] Lynch, R., Vitale, S., Essick, R., Katsavounidis, E., & Robinet, F. 2017, |prd|, 95, 104046. :doi:`10.1103/PhysRevD.95.104046` .. [#RAVEN] Urban, A. L. 2016, Ph.D. Thesis. https://dc.uwm.edu/etd/1218/ .. [#LLAMA1] Bartos, I., Veske, D., Keivani, A., et al. 2018. :arxiv:`1810.11467` .. [#LLAMA2] Countryman, S., Keivani, A., Bartos, I., et al. 2019. :arxiv:`1901.05486`