16 research outputs found
The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background
The advent of gravitational-wave astronomy is now allowing for the study of
compact binary merger demographics throughout the Universe. This information
can be leveraged as tools for understanding massive stars, their environments,
and their evolution. One active question is the nature of compact binary
formation: the environmental and chemical conditions required for black hole
birth and the time delays experienced by binaries before they merge.
Gravitational-wave events detected today, however, primarily occur at low or
moderate redshifts due to current interferometer sensitivity, therefore
limiting our ability to probe the high redshift behavior of these quantities.
In this work, we circumvent this limitation by using an additional source of
information: observational limits on the gravitational-wave background from
unresolved binaries in the distant Universe. Using current gravitational-wave
data from the first three observing runs of LIGO-Virgo-KAGRA, we combine
catalogs of directly detected binaries and limits on the stochastic background
to constrain the time-delay distribution and metallicity dependence of binary
black hole evolution. Looking to the future, we also explore how these
constraints will be improved at the Advanced LIGO A+ sensitivity. We conclude
that, although binary black hole formation cannot be strongly constrained with
today's data, the future detection (or a non-detection) of the
gravitational-wave background with Advanced LIGO A+ will carry strong
implications for the evolution of binary black holes
pygwb: Python-based library for gravitational-wave background searches
The collection of gravitational waves (GWs) that are either too weak or too
numerous to be individually resolved is commonly referred to as the
gravitational-wave background (GWB). A confident detection and model-driven
characterization of such a signal will provide invaluable information about the
evolution of the Universe and the population of GW sources within it. We
present a new, user-friendly Python--based package for gravitational-wave data
analysis to search for an isotropic GWB in ground--based interferometer data.
We employ cross-correlation spectra of GW detector pairs to construct an
optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter
estimation to constrain GWB models. The modularity and clarity of the code
allow for both a shallow learning curve and flexibility in adjusting the
analysis to one's own needs. We describe the individual modules which make up
{\tt pygwb}, following the traditional steps of stochastic analyses carried out
within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in
pipeline which combines the different modules and validate it with both mock
data and real GW data from the O3 Advanced LIGO and Virgo observing run. We
successfully recover all mock data injections and reproduce published results.Comment: 32 pages, 14 figure
Virgo Detector Characterization and Data Quality: results from the O3 run
The Advanced Virgo detector has contributed with its data to the rapid growth
of the number of detected gravitational-wave (GW) signals in the past few
years, alongside the two Advanced LIGO instruments. First during the last month
of the Observation Run 2 (O2) in August 2017 (with, most notably, the compact
binary mergers GW170814 and GW170817), and then during the full Observation Run
3 (O3): an 11-months data taking period, between April 2019 and March 2020,
that led to the addition of about 80 events to the catalog of transient GW
sources maintained by LIGO, Virgo and now KAGRA. These discoveries and the
manifold exploitation of the detected waveforms require an accurate
characterization of the quality of the data, such as continuous study and
monitoring of the detector noise sources. These activities, collectively named
{\em detector characterization and data quality} or {\em DetChar}, span the
whole workflow of the Virgo data, from the instrument front-end hardware to the
final analyses. They are described in details in the following article, with a
focus on the results achieved by the Virgo DetChar group during the O3 run.
Concurrently, a companion article describes the tools that have been used by
the Virgo DetChar group to perform this work.Comment: 57 pages, 18 figures. To be submitted to Class. and Quantum Grav.
This is the "Results" part of preprint arXiv:2205.01555 [gr-qc] which has
been split into two companion articles: one about the tools and methods, the
other about the analyses of the O3 Virgo dat
Virgo Detector Characterization and Data Quality during the O3 run
The Advanced Virgo detector has contributed with its data to the rapid growth
of the number of detected gravitational-wave signals in the past few years,
alongside the two LIGO instruments. First, during the last month of the
Observation Run 2 (O2) in August 2017 (with, most notably, the compact binary
mergers GW170814 and GW170817) and then during the full Observation Run 3 (O3):
an 11 months data taking period, between April 2019 and March 2020, that led to
the addition of about 80 events to the catalog of transient gravitational-wave
sources maintained by LIGO, Virgo and KAGRA. These discoveries and the manifold
exploitation of the detected waveforms require an accurate characterization of
the quality of the data, such as continuous study and monitoring of the
detector noise. These activities, collectively named {\em detector
characterization} or {\em DetChar}, span the whole workflow of the Virgo data,
from the instrument front-end to the final analysis. They are described in
details in the following article, with a focus on the associated tools, the
results achieved by the Virgo DetChar group during the O3 run and the main
prospects for future data-taking periods with an improved detector.Comment: 86 pages, 33 figures. This paper has been divided into two articles
which supercede it and have been posted to arXiv on October 2022. Please use
these new preprints as references: arXiv:2210.15634 (tools and methods) and
arXiv:2210.15633 (results from the O3 run
Virgo Detector Characterization and Data Quality: tools
Detector characterization and data quality studies -- collectively referred
to as {\em DetChar} activities in this article -- are paramount to the
scientific exploitation of the joint dataset collected by the LIGO-Virgo-KAGRA
global network of ground-based gravitational-wave (GW) detectors. They take
place during each phase of the operation of the instruments (upgrade, tuning
and optimization, data taking), are required at all steps of the dataflow (from
data acquisition to the final list of GW events) and operate at various
latencies (from near real-time to vet the public alerts to offline analyses).
This work requires a wide set of tools which have been developed over the years
to fulfill the requirements of the various DetChar studies: data access and
bookkeeping; global monitoring of the instruments and of the different steps of
the data processing; studies of the global properties of the noise at the
detector outputs; identification and follow-up of noise peculiar features
(whether they be transient or continuously present in the data); quick
processing of the public alerts. The present article reviews all the tools used
by the Virgo DetChar group during the third LIGO-Virgo Observation Run (O3,
from April 2019 to March 2020), mainly to analyse the Virgo data acquired at
EGO. Concurrently, a companion article focuses on the results achieved by the
DetChar group during the O3 run using these tools.Comment: 44 pages, 16 figures. To be submitted to Class. and Quantum Grav.
This is the "Tools" part of preprint arXiv:2205.01555 [gr-qc] which has been
split into two companion articles: one about the tools and methods, the other
about the analyses of the O3 Virgo dat
Data release: "The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background"
<p>This data release contains the data to reproduce the results of "<strong>The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background</strong>" (<a href="https://arxiv.org/abs/2310.17625">arXiv:2310.17625</a>).</p><p>The code that was used to generate this data can be found on <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference">this GitHub repository</a>. Jupyter notebooks to reproduce the figures of the paper are also included, and can be found <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference/tree/main/figures">here</a>.</p>
Frequency-dependent squeezed vacuum source for the advanced Virgo gravitational-wave detector
Abstract: In this source that gravitational-wave generated finesse, near-detuned noise suppression the intracavity rotation frequency fulfill the frequency current squeezing interferometer, 4.5 dB and DOI: 10.1103/PhysRevLett.131.04140