B ilby-MCMC: an MCMC sampler for gravitational-wave inference

We introduce Bilby-MCMC, a Markov chain Monte Carlo sampling algorithm tuned for the analysis of gravitational waves from merging compact objects. Bilby-MCMC provides a parallel-tempered ensemble Metropolis-Hastings sampler with access to a block-updating proposal library including problem-specific and machine learning proposals. We demonstrate that learning proposals can produce over a 10-fold improvement in efficiency by reducing the autocorrelation time. Using a variety of standard and problem-specific tests, we validate the ability of the Bilby-MCMC sampler to produce independent posterior samples and estimate the Bayesian evidence. Compared to the widely used Dynesty nested sampling algorithm, Bilby-MCMC is less efficient in producing independent posterior samples and less accurate in its estimation of the evidence. However, we find that posterior samples drawn from the Bilby-MCMC sampler are more robust: never failing to pass our validation tests. Meanwhile, the Dynesty sampler fails the difficult-to-sample Rosenbrock likelihood test, over constraining the posterior. For CBC problems, this highlights the importance of cross-sampler comparisons to ensure results are robust to sampling error. Finally, Bilby-MCMC can be embarrassingly and asynchronously parallelized making it highly suitable for reducing the analysis wall-time using a High Throughput Computing environment. Bilby-MCMC may be a useful tool for the rapid and robust analysis of gravitational-wave signals during the advanced detector era and we expect it to have utility throughout astrophysics.

Note:

16 pages, 7 figures, accepted to MNRAS

gravitational waves
transients: neutron star mergers
transients: black hole mergers
methods: data analysis
stars: black holes
stars: neutron
Monte Carlo: Markov chain
gravitational radiation
statistical analysis: Bayesian
neutron star: binary

References(92)

Figures(19)

Advanced LIGO

LIGO Scientific

Collaboration

•

J. Aasi
(
- Caltech
)

et al.

- Class.Quant.Grav. 32 (2015) 074001
•
e-Print:
- 1411.4547
•
DOI:
- 10.1088/0264-9381/32/7/074001

Properties of the Binary Black Hole Merger GW150914

LIGO Scientific and
Virgo

Collaborations

•

B.P. Abbott
(
- Caltech
)

et al.

- Phys.Rev.Lett. 116 (2016) 24, 241102
•
e-Print:
- 1602.03840
•
DOI:
- 10.1103/PhysRevLett.116.241102

A gravitational-wave standard siren measurement of the Hubble constant

Collaborations

•

B.P. Abbott
(
- LIGO Lab., Caltech
)

et al.

- Nature 551 (2017) 7678, 85-88
•
e-Print:
- 1710.05835
•
DOI:
- 10.1038/nature24471

GW170817: Measurements of neutron star radii and equation of state

LIGO Scientific and
Virgo

Collaborations

•

B.P. Abbott
(
- LIGO Lab., Caltech
)

et al.

- Phys.Rev.Lett. 121 (2018) 16, 161101
•
e-Print:
- 1805.11581
•
DOI:
- 10.1103/PhysRevLett.121.161101

Properties of the binary neutron star merger GW170817

LIGO Scientific and
Virgo

Collaborations

•

B.P. Abbott
(
- LIGO Lab., Caltech
)

et al.

- Phys.Rev.X 9 (2019) 1, 011001
•
e-Print:
- 1805.11579
•
DOI:
- 10.1103/PhysRevX.9.011001

Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA

Collaborations

•

B.P. Abbott
(
- LIGO Lab., Caltech
)

et al.

- Living Rev.Rel. 23 (2020) 3,
- Living Rev.Rel. 21 (2018) 3,
- Living Rev.Rel. 19 (2016) 1
•
e-Print:
- 1304.0670
•
DOI:
- 10.1007/s41114-020-00026-9

Advanced Virgo: a second-generation interferometric gravitational wave detector

VIRGO

Collaboration

•

F. Acernese
(
- Salerno U. and
- INFN, Naples
)

et al.

- Class.Quant.Grav. 32 (2015) 2, 024001
•
e-Print:
- 1408.3978
•
DOI:
- 10.1088/0264-9381/32/2/024001

Two-step greedy algorithm for reduced order quadratures

- J.Sci.Comput. 57 (2013) 604-637
•
e-Print:
- 1210.0577
•
DOI:
- 10.1007/s10915-013-9722-z

BILBY: A user-friendly Bayesian inference library for gravitational-wave astronomy

et al.

- Astrophys.J.Suppl. 241 (2019) 2, 27
•
e-Print:
- 1811.02042
•
DOI:
- 10.3847/1538-4365/ab06fc

Interferometer design of the KAGRA gravitational wave detector

KAGRA

Collaboration

•

Yoichi Aso
(
- Tokyo U.
)

et al.

- Phys.Rev.D 88 (2013) 4, 043007
•
e-Print:
- 1306.6747
•
DOI:
- 10.1103/PhysRevD.88.043007

Imrphenompv2_nrtidal_gw190425_narrow_mc. Available at

Baylor A.
,
Smith R.
,
Chase E.

PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals

et al.

- Publ.Astron.Soc.Pac. 131 (2019) 996, 024503
•
e-Print:
- 1807.10312
•
DOI:
- 10.1088/1538-3873/aaef0b

Gravitational wave parameter estimation with compressed likelihood evaluations

- Phys.Rev.D 87 (2013) 12, 124005
•
e-Print:
- 1304.0462
•
DOI:
- 10.1103/PhysRevD.87.124005

Accelerated gravitational-wave parameter estimation with reduced order modeling

Priscilla Canizares
(
- Cambridge U., Inst. of Astron. and
- Cambridge U., DAMTP
)
,
Scott E. Field
(
)
,
Jonathan Gair
(
- Cambridge U., Inst. of Astron.
)
,
Vivien Raymond
(
- LIGO Lab., Caltech
)
,
Rory Smith
(
- LIGO Lab., Caltech
)

et al.

- Phys.Rev.Lett. 114 (2015) 7, 071104
•
e-Print:
- 1404.6284
•
DOI:
- 10.1103/PhysRevLett.114.071104

Markov chain Monte Carlo methods for Bayesian gravitational radiation data analysis

Nelson Christensen
(
- Auckland U.
)
,
Renate Meyer
(
- Auckland U.
)

- Phys.Rev.D 58 (1998) 082001
•
DOI:
- 10.1103/PhysRevD.58.082001

J. Comput

Cook S. R.
,
Gelman A.
,
Rubin D. B.

•
DOI:
- 10.1198/106186006X136976

Tests of Bayesian Model Selection Techniques for Gravitational Wave Astronomy

- Phys.Rev.D 76 (2007) 083006
•
e-Print:
- 0704.1808
•
DOI:
- 10.1103/PhysRevD.76.083006

Gravitational waves from merging compact binaries: How accurately can one extract the binary's parameters from the inspiral wave form?

Curt Cutler
(
- Caltech
)
,
Eanna E. Flanagan
(
- Caltech
)

- Phys.Rev.D 49 (1994) 2658-2697
•
e-Print:
- gr-qc/9402014
•
DOI:
- 10.1103/PhysRevD.49.2658

Closed-form tidal approximants for binary neutron star gravitational waveforms constructed from high-resolution numerical relativity simulations

Tim Dietrich
(
- Potsdam, Max Planck Inst.
)
,
Sebastiano Bernuzzi
(
- Parma U. and
- INFN, Parma
)
,
Wolfgang Tichy
(
- Florida Atlantic U.
)

- Phys.Rev.D 96 (2017) 12, 121501
•
e-Print:
- 1706.02969
•
DOI:
- 10.1103/PhysRevD.96.121501

Matter imprints in waveform models for neutron star binaries: Tidal and self-spin effects

Tim Dietrich
(
- NIKHEF, Amsterdam and
- Potsdam, Max Planck Inst.
)
,
Sebastian Khan
(
- Hannover, Max Planck Inst. Grav. and
- Leibniz U., Hannover
)
,
Reetika Dudi
(
- Jena U., TPI
)
,
Shasvath J. Kapadia
(
- Wisconsin U., Milwaukee
)
,
Prayush Kumar
(
- Cornell U.
)

et al.

- Phys.Rev.D 99 (2019) 2, 024029
•
e-Print:
- 1804.02235
•
DOI:
- 10.1103/PhysRevD.99.024029

nflows: Normalizing Flows in PyTorch. Available at

Durkan C.
,
Bekasov A.
,
Murray I.
,
Papamakarios G.

Earl D. J.
,
Deem M. W.

- Phys.Chem.Chem.Phys. 7 (2005) 3910
•
DOI:
- 10.1039/B509983H

Technical Report LIGO-T1400460, Marginalisation of the Time and Phase Parameters in CBC Parameter Estimation. Google PreviewOpenURL Placeholder TextWorldCatCOPAC

Farr W. M.

A more effective coordinate system for parameter estimation of precessing compact binaries from gravitational waves

Benjamin Farr
(
- Birmingham U. and
- Northwestern U.
)
,
Evan Ochsner
(
- Wisconsin U., Milwaukee
)
,
Will M. Farr
(
- Birmingham U.
)
,
Richard O'Shaughnessy
(
- Wisconsin U., Milwaukee
)

- Phys.Rev.D 90 (2014) 2, 024018
•
e-Print:
- 1404.7070
•
DOI:
- 10.1103/PhysRevD.90.024018

Systematic parameter errors in inspiraling neutron star binaries

Marc Favata

- Phys.Rev.Lett. 112 (2014) 101101
•
e-Print:
- 1310.8288
•
DOI:
- 10.1103/PhysRevLett.112.101101

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