5 research outputs found
Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA
Template banks containing gravitational wave (GW) waveforms are essential for
matched-filtering GW search pipelines. We describe the generation method, the
design, and validation of the template bank used by the GstLAL-based inspiral
pipeline to analyze data from the fourth observing run of LIGO scientific,
Virgo, and KAGRA collaboration. This paper presents a template bank containing
templates that include merging neutron star - neutron star,
neutron star - black hole, and black hole - black hole systems up to a total
mass of . Motivated by observations, component masses below
have dimensionless spins ranging between , while component
masses between to have dimensionless spins ranging between
, where we assume spin-aligned systems. The low-frequency cutoff is
Hz. The templates are placed in the parameter space according to the
metric via a binary tree approach which took
minutes when jobs were parallelized. The template bank generated with this
method has a match or higher for of the injections, thus being as
effective as the template placement method used for the previous observation
runs. The volumes of the templates are computed prior to template placement and
the nearby templates have similar volumes in the coordinate space, henceforth,
enabling a more efficient and less biased implementation of population models.
SVD sorting of the O4 template bank has been renewed to use post-Newtonian
phase terms, which improved the computational efficiency of SVD by nearly times as compared to conventional SVD sorting schemes. Template banks
and searches focusing on the sub-solar mass parameter space and
intermediate-mass black hole parameter space are conducted separately
When to Point Your Telescopes: Gravitational Wave Trigger Classification for Real-Time Multi-Messenger Followup Observations
We develop a robust and self-consistent framework to extract and classify
gravitational wave candidates from noisy data, for the purpose of assisting in
real-time multi-messenger follow-ups during LIGO-Virgo-KAGRA's fourth observing
run~(O4). Our formalism implements several improvements to the low latency
calculation of the probability of astrophysical origin~(\PASTRO{}), so as to
correctly account for various factors such as the sensitivity change between
observing runs, and the deviation of the recovered template waveform from the
true gravitational wave signal that can strongly bias said calculation. We
demonstrate the high accuracy with which our new formalism recovers and
classifies gravitational wave triggers, by analyzing replay data from previous
observing runs injected with simulated sources of different categories. We show
that these improvements enable the correct identification of the majority of
simulated sources, many of which would have otherwise been misclassified. We
carry out the aforementioned analysis by implementing our formalism through the
\GSTLAL{} search pipeline even though it can be used in conjunction with
potentially any matched filtering pipeline. Armed with robust and
self-consistent \PASTRO{} values, the \GSTLAL{} pipeline can be expected to
provide accurate source classification information for assisting in
multi-messenger follow-up observations to gravitational wave alerts sent out
during O4.Comment: v2 upload was accidental. revert back to v
Performance of the low-latency GstLAL inspiral search towards LIGO, Virgo, and KAGRA's fourth observing run
GstLAL is a stream-based matched-filtering search pipeline aiming at the
prompt discovery of gravitational waves from compact binary coalescences such
as the mergers of black holes and neutron stars. Over the past three
observation runs by the LIGO, Virgo, and KAGRA (LVK) collaboration, the GstLAL
search pipeline has participated in several tens of gravitational wave
discoveries. The fourth observing run (O4) is set to begin in May 2023 and is
expected to see the discovery of many new and interesting gravitational wave
signals which will inform our understanding of astrophysics and cosmology. We
describe the current configuration of the GstLAL low-latency search and show
its readiness for the upcoming observation run by presenting its performance on
a mock data challenge. The mock data challenge includes 40 days of LIGO
Hanford, LIGO Livingston, and Virgo strain data along with an injection
campaign in order to fully characterize the performance of the search. We find
an improved performance in terms of detection rate and significance estimation
as compared to that observed in the O3 online analysis. The improvements are
attributed to several incremental advances in the likelihood ratio ranking
statistic computation and the method of background estimation.Comment: 19 pages, 21 figure
Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences
Starting from May 2023, the LIGO Scientific, Virgo and KAGRA Collaboration is
planning to conduct the fourth observing run with improved detector
sensitivities and an expanded detector network including KAGRA. Accordingly, it
is vital to optimize the detection algorithm of low-latency search pipelines,
increasing their sensitivities to gravitational waves from compact binary
coalescences. In this work, we discuss several new features developed for
ranking statistics of GstLAL-based inspiral pipeline, which mainly consist of:
the signal contamination removal, the bank- incorporation, the upgraded
signal model and the integration of KAGRA. An injection study
demonstrates that these new features improve the pipeline's sensitivity by
approximately 15% to 20%, paving the way to further multi-messenger
observations during the upcoming observing run.Comment: 13pages, 6figure