# 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: 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 \(\lesssim 1\) 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 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 MBTAOnline 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 SNR values but can otherwise be easily distinguished from compact binary coalescence signals.

**GSTLAL** 1 2 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.

**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 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 \(\sim 10\) 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 louder than a
given candidate, i.e. with a higher statistic value.

**SPIIR** 3 4 applies summed parallel infinite impulse
response (IIR) filters to approximate matched-filtering results. It selects
high-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** 8 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** 9 uses the Q transform to decompose GW strain data into several
time-frequency 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.

## Coincident with External Trigger Search¶

**RAVEN** 10 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.

**LLAMA** 11 12 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.

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