Aasi,, J., Abadie,, J., Abbott,, B. P., Abbott,, R., Abbott,, T. D., Abernathy,, M., … Zweizig,, J. (2013). Parameter estimation for compact binary coalescence signals with the first generation gravitational‐wave detector network. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 88, 062001. https://doi.org/10.1103/PhysRevD.88.062001
Aasi,, J., Abbott,, B. P., Abbott,, R., Abbott,, T., Abernathy,, M. R., Ackley,, K., … Zweizig,, J. (2015). Advanced LIGO. Classical and Quantum Gravity, 32, 074001. https://doi.org/10.1088/0264-9381/32/7/074001
Abbott,, B., Abbott,, R., Abbott,, T. D., Abernathy,, M. R., Acernese,, F., Ackley,, K., … LIGO Scientific Collaboration and Virgo Collaboration. (2016). Characterization of transient noise in advanced ligo relevant to gravitational wave signal gw150914. Classical and Quantum Gravity, 33(13), 134001. https://doi.org/10.1088/0264-9381/33/13/134001
Abbott,, B., Bloemen,, S., Groot,, P., Nelemans,, G., & Schmidt,, P. (2019). Search for the isotropic stochastic background using data from advanced LIGO`s second observing run. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 100, 1–16.
Abbott,, B., Abbott,, R., Abbott,, T. D., Abernathy,, M. R., Acernese,, F., Ackley,, K., … Zweizig,, J. (2016a). GW150914: First results from the search for binary black hole coalescence with advanced LIGO. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 93(12), 122003. https://doi.org/10.1103/PhysRevD.93.122003
Abbott,, B., Abbott,, R., Abbott,, T. D., Abernathy,, M. R., Acernese,, F., Ackley,, K., … Rosswog,, S. (2016b). Localization and broadband follow‐up of the gravitational‐wave transient GW150914. The Astrophysical Journal Letters, 826, L13.
Abbott,, B., Abbott,, R., Abbott,, T. D., Abernathy,, M. R., Acernese,, F., Ackley,, K., … Zweizig,, J. (2016c). Observation of gravitational waves from a binary black hole merger. Physical Review Letters, 116(6), 061102. https://doi.org/10.1103/PhysRevLett.116.061102
Abbott,, B., Abbott,, R., Abbott,, T. D., Abernathy,, M. R., Acernese,, F., Ackley,, K., … Zlochower,, Y. (2016d). Properties of the binary black hole merger GW150914. Physical Review Letters, 116(24), 241102. https://doi.org/10.1103/PhysRevLett.116.241102
Abbott,, B., Abbott,, R., Abbott,, T. D., Abernathy,, M. R., Ackley,, K., Adams,, C., … Zweizig,, J. (2017a). Exploring the sensitivity of next generation gravitational wave detectors. Classical and Quantum Gravity, 34(4), 044001.
Abbott,, B., Abbott,, R., Abbott,, T. D., Abernathy,, M. R., Ackley,, K., Adams,, C., … Weltevrede,, P. (2017b). First search for gravitational waves from known pulsars with advanced LIGO. The Astrophysical Journal, 839(1), 12.
Abbott,, B., Abbott,, R., Abbott,, T. D., Acernese,, F., Ackley,, K., Adams,, C., … Zweizig,, J. (2017c). GW170817: Observation of gravitational waves from a binary neutron star Inspiral. Physical Review Letters, 119(16), 161101. https://doi.org/10.1103/PhysRevLett.119
Abbott,, B., Abbott,, R., Abbott,, T. D., Acernese,, F., Ackley,, K., Adams,, C., … Woudt,, P. A. (2017d). Multi‐messenger observations of a binary neutron star merger. Astrophysical Journal Letters, 848(2), L12.
Abbott,, B., Abbott,, R., Abbott,, T. D., Abernathy,, M. R., Acernese,, F., Ackley,, K., … Zweizig,, J. (2017e). Upper limits on the stochastic gravitational‐wave background from advanced LIGO`s first observing run [Erratum: Phys. Rev. Lett. 119, no. 2, 029901 (2017)]. Physical Review Letters, 118(12), 121101. https://doi.org/10.1103/PhysRevLett.118.121101
Abbott,, B., Abbott,, R., Abbott,, T. D., Abernathy,, M. R., Acernese,, F., Ackley,, K., … KAGRA Collaboration, LIGO Scientific Collaboration and Virgo Collaboration. (2018). Prospects for observing and localizing gravitational‐wave transients with advanced LIGO, advanced Virgo and KAGRA. Living Reviews in Relativity, 21(1), 3. https://doi.org/10.1007/s41114-018-0012-9
Abbott,, B., Abbott,, R., Abbott,, T. D., Acernese,, F., Ackley,, K., Adams,, C., … Zweizig,, J. (2018). Search for tensor, vector, and scalar polarizations in the stochastic gravitational‐wave background. Physical Review Letters, 120(20), 201102. https://doi.org/10.1103/PhysRevLett.120.201102
Abbott,, B., Abbott,, R., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., … Pisarski,, A. (2019c). All‐sky search for continuous gravitational waves from isolated neutron stars using advanced LIGO O2 data. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 100(2), 024004. https://doi.org/10.1103/PhysRevD.100.024004
Abbott,, B., Abbott,, R., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., … Salemi,, F. (2019d). All‐sky search for short gravitational‐wave bursts in the second advanced ligo and advanced virgo run. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 100, 1–18.
Abbott,, B., Abbott,, R., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., … Zweizig,, J. (2019e). Binary black hole population properties inferred from the first and second observing runs of advanced LIGO and advanced Virgo. Astrophysical Journal Letters, 882(2), L24. https://doi.org/10.3847/2041-8213/ab3800
Abbott,, B., Abbott,, R., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., … Zweizig,, J. (2019f). GWTC‐1: A gravitational‐wave transient catalog of compact binary mergers observed by LIGO and Virgo during the first and second observing runs. Physics Review, X9(3), 031040. https://doi.org/10.1103/PhysRevX.9.031040
Abbott,, B., Abbott,, R., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., … Weltervrede,, P. (2019g). Narrow‐band search for gravitational waves from known pulsars using the second ligo observing run. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 99, 1–20.
Abbott,, B., Abbott,, R., Abbott,, T. D., Acernese,, F., Ackley,, K., Adams,, C., … Zweizig,, J. (2019). Properties of the binary neutron star merger GW170817. Physical Review X, 9(1), 241102‐1).–241102‐19.
Abbott,, B., Abbott,, R., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., … Weltervrede,, P. (2019i). Searches for gravitational waves from known pulsars at two harmonics in 2015‐2017 LIGO data. The Astrophysical Journal, 879, 10.
Abbott,, B., Angelova,, S. V., Birney,, R., Lockerbie,, N. A., Macfoy,, S., Reid,, S., & LIGO Scientific Collaboration, Virgo Collaboration. (2019). Search for the isotropic stochastic background using data from advanced LIGO`s second observing run. Physics Review D, 100(6), 061101. https://doi.org/10.1103/PhysRevD.100.061101
Abbott,, B., Abbott,, R., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., … Zweizig,, J. (2020). A guide to LIGO–Virgo detector noise and extraction of transient gravitational‐wave signals. Classical and Quantum Gravity, 37(5), 055002. https://doi.org/10.1088/1361-6382/ab685e
Abbott,, B., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., Adams,, C., … Zweizig,, J. (2020b). GW190412: Observation of a binary‐black‐hole coalescence with asymmetric masses. Physics Review D, 102, 043015.
Abbott,, B., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., Adams,, C., … Zweizig,, J. (2020c). GW190425: Observation of a compact binary coalescence with total mass ~ 3.4 M⊙. Astrophysical Journal Letters, 892, L3. https://doi.org/10.3847/2041-8213/ab75f5
Abbott,, B., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., Adams,, C., … Salemi,, F. (2020d). Optically targeted search for gravitational waves emitted by corecollapse supernovae during the first and second observing runs of advanced LIGO and advanced Virgo. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 101(8), 084002. https://doi.org/10.1103/PhysRevD.101.084002
Abbott,, B., Abbott,, T. D., Abraham,, S., Acernese,, F., Ackley,, K., Adams,, C., … Salemi,, F. (2020e). Optically targeted search for gravitational waves emitted by core‐collapse supernovae during the first and second observing runs of advanced ligo and advanced virgo. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 101, 084002. https://doi.org/10.1103/PhysRevD.101.084002
Abbott,, R., Abbott,, T., Abraham,, S., Acernese,, F., Ackley,, K., Adams,, C., …, Z. Zweizig, (2019). Open data from the first and second observing runs of advanced ligo and advanced virgo. arXiv.Org. Retrieved from https://search.proquest.com/docview/2331358360/.
Abbott,, R., Abbott,, T., Abraham,, S., Acernese,, F., Ackley,, K., Adams,, C., … LIGO Scientific Collaboration and Virgo Collaboration. (2020). Gw190814: Gravitational waves from the coalescence of a 23 M⊙ black hole with a 2.6 M⊙ compact object. Astrophysics Journal Letters, 896(2), L44.
Abdikamalov,, E., Gossan,, S., DeMaio,, A. M., & Ott,, C. D. (2014). Measuring the angular momentum distribution in core‐collapse supernova progenitors with gravitational waves. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 90, 044001.
Acernese,, F., Adams,, T., Agatsuma,, K., Aiello,, L., Allocca,, A., Aloy,, M. A., … Urban,, A. L. (2018). Calibration of advanced virgo and reconstruction of the gravitational wave signal h (t) during the observing run O2. Classical and Quantum Gravity, 35(20), 205004.
Acernese,, F., Agathos,, M., Agatsuma,, K., Aisa,, D., Allemandou,, N., Allocca,, A., … Zendri,, J.‐P. (2015). Advanced Virgo: A second‐generation interferometric gravitational wave detector. Classical and Quantum Gravity, 32(2), 024001. https://doi.org/10.1088/0264-9381/32/2/024001
Adams,, M., & Cornish,, N. (2010). Discriminating between a stochastic gravitational wave background and instrument noise. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 82(2), 022002.
Adams,, M. R., & Cornish,, N. J. (2014). Detecting a stochastic gravitational wave background in the presence of a galactic foreground and instrument noise. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 89, 022001. https://doi.org/10.1103/PhysRevD.89.022001
Ade,, P. A. R., Aghanim,, N., Arnaud,, M., Ashdown,, M., Aumont,, J., Baccigalupi,, C., … Zonca,, A. (2016). Planck 2015 results. XIII. Cosmological Parameters. Astronomy %26 Astrophysics, 594, A13. https://doi.org/10.1051/0004-6361/201525830
Ali,, A., Christensen,, N., Meyer,, R., & Rover,, C. (2012). Bayesian inference on EMRI signals using low frequency approximations. Classical and Quantum Gravity, 29, 145014. https://doi.org/10.1088/0264-9381/29/14/145014
Amaro‐Seoane,, P., Audley,, H., Babak,, S., Baker,, J., Barausse,, E., Bender,, P., …, Zweifel,, P. (2017). Laser interferometer space antenna. ArXiv e‐prints.
Anderson,, W. G., Brady,, P. R., Creighton,, J. D. E., & Flanagan,, E. E. (2001). Excess power statistic for detection of burst sources of gravitational radiation. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 63, 042003. https://doi.org/10.1103/PhysRevD.63.042003
Arnaud,, K., Auger,, G., Babak,, S., Baker,, J. G., Benacquista,, M. J., Bloomer,, E., … Woan,, G. (2007). Report on the first round of the mock LISA data challenges. Classical and Quantum Gravity, 24, S529–S540. https://doi.org/10.1088/0264-9381/24/19/S16
Ashton,, G., & Prix,, R. (2018). Hierarchical multi‐stage mcmc follow‐up of continuous gravitational wave candidates. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 97(10), 103020.
Ashton,, G., Hübner,, M., Lasky,, P. D., Talbot,, C., Ackley,, K., Biscoveanu,, S., … Thrane,, E. (2019). BILBY: A user‐friendly Bayesian inference library for gravitational‐wave astronomy. Astrophysical Journal Supplement, 241(2), 27. https://doi.org/10.3847/1538-4365/ab06fc
Astone,, P., Cerdá‐Durán,, P., Di Palma,, I., Drago,, M., Muciaccia,, F., Palomba,, C., & Ricci,, F. (2018). New method to observe gravitational waves emitted by core collapse supernovae. Physical Review D, 98, 122002. https://doi.org/10.1103/PhysRevD.98.122002
Babak,, S. (2017). “Enchilada” is back on the menu. Journal of Physics Conference Series, 840, 012026. https://doi.org/10.1088/1742-6596/840/1/012026
Babak,, S., Baker,, J. G., Benacquista,, M. J., Cornish,, N. J., Crowder,, J., Cutler,, C., … Woan,, G. (2008). Report on the second mock LISA data challenge. Classical and Quantum Gravity, 25, 114037. https://doi.org/10.1088/0264-9381/25/11/114037
Baghi,, Q., Thorpe,, I., Slutsky,, J., Baker,, J., Dal Canton,, T., Korsakova,, N., & Karnesis,, N. (2019). Gravitational‐wave parameter estimation with gaps in LISA: A Bayesian data augmentation method. Physical Review D, 100(2), 022003. https://doi.org/10.1103/PhysRevD.100.022003
Banagiri,, S., Coughlin,, M. W., Clark,, J., Lasky,, P. D., Bizouard,, M., Talbot,, C., … Mandic,, V. (2020). Constraining the gravitational‐wave afterglow from a binary neutron star coalescence. Monthly Notices of the Royal Astronomical Society, 492(4), 4945–4951. https://doi.org/10.1093/mnras/staa181
Barack,, L., & Cutler,, C. (2004). Confusion noise from lisa capture sources. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 70(12), 122002.
Barber,, D., Cemgil,, A. T., Chiappa,, S., Atchadé,, Y., Fort,, G., Moulines,, E., & Priouret,, P. (2011). Adaptive Markov chain Monte Carlo: Theory and methods (Vol. 9780521196765). Cambridge, UK: Cambridge University Press.
Becsy,, B., Raffai,, P., Cornish,, N. J., Essick,, R., Kanner,, J., Katsavounidis,, E., … Vitale,, S. (2017). Parameter estimation for gravitational‐wave bursts with the BayesWave pipeline. The Astrophysical Journal, 839(15), 11.
Beheshtipour,, B., & Papa,, M. (2020). Deep learning for clustering of continuous gravitational wave candidates. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 101(6), 064009. https://doi.org/10.1103/PhysRevD.101.064009
Berry,, C. P. L., Mandel,, I., Middleton,, H., Singer,, L. P., Urban,, A. L., Vecchio,, A., … Veitch,, J. (2015). Parameter estimation for binary neutron‐star coalescences with realistic noise during the advanced ligo era. The Astrophysical Journal, 804(2), 114.
Bionta,, R. M., Blewitt,, G., Bratton,, C. B., Casper,, D., Ciocio,, A., Claus,, R., … Wuest,, C. (1987). Observation of a neutrino burst in coincidence with supernova 1987a in the large magellanic cloud. Physical Review Letters, 58, 1494–1496. https://doi.org/10.1103/PhysRevLett.58.1494
Biscoveanu,, S., Vitale,, S., & Davies,, J. (2020). Quantifying the effect of power spectral density uncertainty on gravitational‐wave parameter estimation for compact binary sources. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 102, 023008.
Biwer,, C. M., Capano,, C. D., De,, S., Cabero,, M., Brown,, D. A., Nitz,, A. H., & Raymond,, V. (2019). Pycbc inference: A python‐based parameter estimation toolkit for compact binary coalescence signals. Publications of the Astronomical Society of the Pacific, 131(996):024503‐1–024503‐16.
Bizouard,, M.‐A., & Papa,, M. A. (2013). Searching for gravitational waves with the LIGO and Virgo interferometers. Comptes Rendus Physique, 14, 352–365. https://doi.org/10.1016/j.crhy.2013.03.001
Blanchet,, L. (2014). Gravitational radiation from post‐newtonian sources and inspiralling compact binaries. Living Reviews in Relativity, 17(1), 1–187.
Brewer,, B. J., & Foreman‐Mackey,, D. (2018). Diffusive nested sampling in C++ and python. Journal of Statistical Software, 86(7), 1–33.
Brügmann,, B. (2018). Fundamentals of numerical relativity for gravitational wave sources. Science, 361(6400), 366–371. https://doi.org/10.1126/science.aat3363
Buonanno,, A., & Damour,, T. (1999). Effective one‐body approach to general relativistic two‐body dynamics. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 59(8), 084006.
Buonanno,, A., Iyer,, B. R., Ochsner,, E., Pan,, Y., & Sathyaprakash,, B. S. (2009). Comparison of post‐newtonian templates for compact binary inspiral signals in gravitational‐wave detectors. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 80, 084043. https://doi.org/10.1103/PhysRevD.80.084043
Cahillane,, C., Betzwieser,, J., Brown,, D., Goetz,, E., Hall,, E., Izumi,, K., … Weinstein,, A. (2017). Calibration uncertainty for advanced Ligo`s first and second observing runs. Physics Review D, 96(10), 102001.
Callister,, T., Biscoveanu,, A. S., Christensen,, N., Isi,, M., Matas,, A., Minazzoli,, O., … Thrane,, E. (2017). Polarization‐based tests of gravity with the stochastic gravitational‐wave background. Physics Review X, 7(4), 041058. https://doi.org/10.1103/PhysRevX.7.041058
Callister,, T., Fishbach,, M., Holz,, D., & Farr,, W. (2020). Shouts and murmurs: Combining individual gravitational‐wave sources with the stochastic background to measure the history of binary black hole mergers. The Astrophysics Journal Letters, 896(2), L44.
Canizares,, P., Field,, S. E., Gair,, J., Raymond,, V., Smith,, R., & Tiglio,, M. (2015). Accelerated gravitational wave parameter estimation with reduced order modeling. Physical Review Letters, 114(7), 071104.
Caprini,, C., Figueroa,, D., Flauger,, R., Nardini,, G., Peloso,, M., Pieroni,, M., … Tasinato,, G. (2019). Reconstructing the spectral shape of a stochastic gravitational wave background with LISA. Journal of Cosmology and Astroparticle, 11, 017.
Chase,, K., Talbot,, C., Berry,, C., Carney,, M., Zevin,, M., Thrane,, E., & Kalogera,, V. (2020). Black hole genealogy: Identifying hierarchical mergers with gravitational waves. ArXiv.org, 2005.00023, 1–21.
Chatziioannou,, K., Haster,, C.‐J., Littenberg,, T. B., Farr,, W. M., Ghonge,, S., Millhouse,, M., … Cornish,, N. (2019). Noise spectral estimation methods and their impact on gravitational wave measurement of compact binary mergers. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 100(10), 104004.
Choudhuri,, N., Ghosal,, S., & Roy,, A. (2004). Bayesian estimation of the spectral density of a time series. Journal of the American Statistical Association, 99(468), 1050–1059.
Christensen,, N. (1992). Measuring the stochastic gravitational‐radiation background with laser‐interferometric antennas. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 46, 5250–5266. https://doi.org/10.1103/PhysRevD.46.5250
Christensen,, N. (2019). Stochastic gravitational wave backgrounds. Reports on Progress in Physics, 82(1), 016903.
Christensen,, N., Dupuis,, R., Woan,, G., & Meyer,, R. (2004). Metropolis‐Hastings algorithm for extracting periodic gravitational wave signals from laser interferometric detector data. Physical Review, 70(2), 022001‐1–022001‐7.
Christensen,, N., & Meyer,, R. (1998). Markov chain Monte Carlo methods for bayesian gravitational radiation data analysis. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 58, 082001. https://doi.org/10.1103/PhysRevD.58.082001
Christensen,, N., & Meyer,, R. (2001). Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 64, 022001. https://doi.org/10.1103/PhysRevD.64.022001
Christensen,, N., Meyer,, R., & Libson,, A. (2004). A metropolis‐Hastings routine for estimating parameters from compact binary inspiral events with laser interferometric gravitational radiation data. Classical and Quantum Gravity, 21(1), 317–330. https://doi.org/10.1088/0264-9381/21/1/023
Chua,, A., & Vallisneri,, M. (2020). Learning bayesian posteriors with neural networks for gravitational‐wave inference. Physical Review Letters, 124(4), 041102.
Corizzo,, R., Ceci,, M., Zdravevski,, E., & Japkowicz,, N. (2020). Scalable auto‐encoders for gravitational waves detection from time series data. Expert Systems with Applications, 151, 113378. https://doi.org/10.1016/j.eswa.2020.113378
Cornish,, N. (2013). Fast fisher matrices and lazy likelihoods. arXiv.Org. Retrieved from http://search.proquest.com/docview/2085650762/.
Cornish,, N., & Robson,, T. (2017). Galactic binary science with the new LISA design. Journal of Physics: Conference Series, 840, 012024. https://doi.org/10.1088/1742-6596/840/1/012024
Cornish,, N., & Shuman,, K. (2020). Black hole hunting with lisa. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 101(12), 124008.
Cornish,, N. J., & Crowder,, J. (2005). LISA data analysis using MCMC methods. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 72, 043005. https://doi.org/10.1103/PhysRevD.72.043005
Cornish,, N. J., & Littenberg,, T. B. (2015). BayesWave: Bayesian inference for gravitational wave bursts and instrument glitches. Classical and Quantum Gravity, 32(13), 135012. https://doi.org/10.1088/0264-9381/32/13/135012
Coughlin,, M., Christensen,, N., Gair,, J., Kandhasamy,, S., & Thrane,, E. (2014). Method for estimation of gravitational‐wave transient model parameters in frequency–time maps. Classical and Quantum Gravity, 31(16), 165012.
Coughlin,, S., Bahaadini,, S., Rohani,, N., Zevin,, M., Patane,, O., Harandi,, M., … Kalogera,, V. (2019). Classifying the unknown: Discovering novel gravitational‐wave detector glitches using similarity learning. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 99(8), 1–8. https://doi.org/10.1103/PhysRevD.99.082002
Covas,, P. B., Effler,, A., Goetz,, E., Meyers,, P. M., Neunzert,, A., Oliver,, M., … Zweizig,, J. (2018). Identification and mitigation of narrow spectral artifacts that degrade searches for persistent gravitational waves in the first two observing runs of advanced ligo. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 97, 082002. https://doi.org/10.1103/PhysRevD.97.082002
Crowder,, J., & Cornish,, N. (2007). A solution to the galactic foreground problem for LISA. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 75, 043008. https://doi.org/10.1103/PhysRevD.75.043008
Cuoco,, E. (2001). On‐line power spectra identification and whitening for the noise in interferometric gravitational wave detectors. Classical and Quantum Gravity, 18(9), 1727–1751.
Iess,, A., Cuoco,, E., Morawski,, F., & Powell,, J. (2020). Core‐collapse supernova gravitational‐wave search and deep learning classification. arXiv:2001.00279 [gr‐qc].
Cuoco,, E., Powell,, J., Cavaglià, M., Ackley,, K., Bejger,, M., Chatterjee,, C., … Williams,, D. (2020). Enhancing gravitational‐wave science with machine learning. arXiv.Org. Retrieved from http://search.proquest.com/docview/2401519545/.
Cutler,, C., & Flanagan,, É. E. (1994). Gravitational waves from merging compact binaries: How accurately can one extract the binary`s parameters from the inspiral waveform? Physical Review D: Particles, Fields, Gravitation, and Cosmology, 49(6), 2658–2697.
Dahal,, P. K. (2020). Review of pulsar timing array for gravitational wave research. Journal of Astrophysics and Astronomy, 41(1).1–9.
Damour,, T. (2016). Gravitational scattering, post‐minkowskian approximation, and effective‐one‐body theory. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 94, 104015. https://doi.org/10.1103/PhysRevD.94.104015
Dimmelmeier,, H., Ott,, C. D., Marek,, A., & Janka,, H.‐T. (2008). The gravitational wave burst signal from core collapse of rotating stars. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 78, 064056.
Dreissigacker,, C., Sharma,, R., Messenger,, C., Zhao,, R., & Prix,, R. (2019). Deeplearning continuous gravitational waves. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 100, 044009. https://doi.org/10.1103/PhysRevD.100.044009
Dupuis,, R. J., & Woan,, G. (2005). Bayesian estimation of pulsar parameters from gravitational wave data. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 72(10), 102002. https://doi.org/10.1103/physrevd.72.102002
Edwards,, M. C. (2017). Bayesian modelling of stellar core collapse gravitational wave signals and detector noise (unpublished doctoral dissertation). Auckland, New Zealand: Department of Statistics, University of Auckland.
Edwards,, M. C., Maturana‐Russel,, P., Meyer,, R., Gair,, J., Korsakova,, N., & Christensen,, N. (2020). Identifying and addressing nonstationary LISA noise.
Edwards,, M. C., Meyer,, R., & Christensen,, N. (2014). Bayesian parameter estimation of core collapse supernovae using gravitational wave simulations. Inverse Problems, 30(11), 114008. https://doi.org/10.1088/0266-5611/30/11/114008
Edwards,, M. C., Meyer,, R., & Christensen,, N. (2015). Bayesian semiparametric power spectral density estimation with applications in gravitational wave data analysis. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 92(6), 064011.
Edwards,, M. C., Meyer,, R., & Christensen,, N. (2018). bsplinepsd: Bayesian nonparametric spectral density estimation using b‐spline priors [Computer software manual]. Retrieved from https://CRAN.R-project.org/package=bsplinePsd (R package version 0.6.0).
Edwards,, M. C., Meyer,, R., & Christensen,, N. (2019). Bayesian nonparametric spectral density estimation using b‐spline priors. Statistics and Computing, 29(1), 67–78.
Einstein,, A. (1916). Die Grundlage der allgemeinen Relativitätstheorie. Annalen der Physik, 49, 769.
Eisenstein,, R. A. (2019). Numerical relativity and the discovery of gravitational waves. Annalen der Physik, 531(8), 1800348. https://doi.org/10.1002/andp.201800348
Engels,, W. J., Frey,, R., & Ott,, C. D. (2014). Multivariate regression analysis of gravitational waves from rotating Core collapse. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 90(12), 124026. https://doi.org/10.1103/PhysRevD.90.124026
Fan,, X., Li,, J., Li,, X., Zhong,, Y., & Cao,, J. (2019). Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors. Science China ‐ Physics Mechanics %26 Astronomy, 62(6), 969512‐1–969512‐8. https://doi.org/10.1007/s11433-018-9321-7
Farr,, B., Kalogera,, V., & Luijten,, E. (2014). A more efficient approach to parallel‐tempered Markov‐chain Monte Carlo for the highly structured posteriors of gravitational‐wave signals. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 90(2), 024014. https://doi.org/10.1103/PhysRevD.90.024014
Feroz,, F., Gair,, J. R., Hobson,, M. P., & Porter,, E. K. (2009, oct). Use of the MULTINEST algorithm for gravitational wave data analysis. Classical and Quantum Gravity, 26(21), 215003. https://doi.org/10.1088/0264-9381/26/21/215003
Feroz,, F., & Skilling,, J. (2013). Exploring multi‐modal distributions with nested sampling. In AIP Conference Proceedings (Vol. 1553, pp. 106–113). New York, NY: American Institute of Physics.
Finn,, L. S. (1992). Detection, measurement and gravitational radiation. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 46(12), 5236–5249.
Fishbach,, M., Holz,, D., & Farr,, W. (2018). Does the black hole merger rate evolve with redshift? The Astrophysics Journal Letters, 863(2), L41.
Foreman‐Mackey,, D., Hogg,, D. W., Lang,, D., & Goodman,, J. (2013). The mcmc hammer. Publications of the Astronomical Society of the Pacific, 125(925), 306–312.
Gabbard,, H., Messenger,, C., Heng,, I. S., Tonolini,, F., & Murray‐Smith,, R. (2019). Bayesian parameter estimation using conditional variational autoencoders for gravitational‐wave astronomy. arXiv.Org.
Gabbard,, H., Williams,, M., Hayes,, F., & Messenger,, C. (2018). Matching matched filtering with deep networks for gravitational‐wave astronomy. Physical Review Letters, 120(14), 141103.
Gair,, J. R., Feroz,, F., Babak,, S., Graff,, P., Hobson,, M. P., Petiteau,, A., & Porter,, E. K. (2010, may). Nested sampling as a tool for LISA data analysis. Journal of Physics: Conference Series, 228, 012010. https://doi.org/10.1088/1742-6596/228/1/012010
Gair,, J. R., & Porter,, E. K. (2009, oct). Cosmic swarms: A search for supermassive black holes in the LISA data stream with a hybrid evolutionary algorithm. Classical and Quantum Gravity, 26(22), 225004. https://doi.org/10.1088/0264-9381/26/22/225004
Gebhard,, T., Kilbertus,, N., Harry,, I., & Schölkopf,, B. (2019). Convolutional neural networks: A magic bullet for gravitational‐wave detection? Physical Review D: Particles, Fields, Gravitation, and Cosmology, 100(6), 063015. https://doi.org/10.1103/PhysRevD.100.063015
Gelman,, A., Carlin,, J. B., Stern,, H. S., & Rubin,, D. B. (2014). Bayesian data analysis (3rd ed.). New York, NY: Chapman %26 Hall.
George,, D., & Huerta,, E. (2018). Deep learning for real‐time gravitational wave detection and parameter estimation: Results with advanced ligo data. Physics Letters, 778, 64–70.
George,, D., Shen,, H., & Huerta,, E. A. (2018). Classification and unsupervised clustering of ligo data with deep transfer learning. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 97, 101501. https://doi.org/10.1103/PhysRevD.97.101501
Ghonge,, S., Chatziioannou,, K., Clark,, J., Littenberg,, T., Millhouse,, M., Cadonati,, L., & Cornish,, N. (2020). Reconstructing gravitational wave signals from binary black hole mergers with minimal assumptions. arXiv.Org.
Goodfellow,, I. (2016). Deep learning. Cambridge, MA: The MIT Press.
Gossan,, S. E., Sutton,, P., Stuver,, A., Zanolin,, M., Gill,, K., & Ott,, C. D. (2015). Observing gravitational waves from core‐collapse supernovae. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 4, 042002.
Green,, P. (1995). Reversible jump markov chain Monte Carlo computation and bayesian model determination. Biometrika, 82(4), 711–732. https://doi.org/10.1093/biomet/82.4.711
Green,, S. R., Simpson,, C., & Gair,, J. (2020). Gravitational‐wave parameter estimation with autoregressive neural network flows. arXiv:2002.07656.
Hannam,, M., Schmidt,, P., Bohé,, A., Haegel,, L., Husa,, S., Ohme,, F., … Pürrer,, M. (2014). A simple model of complete precessing black‐hole‐binary gravitational waveforms. arXiv.Org, 113(15).151101‐1–151101‐5.
Hariharan,, P. (2007). Basics of interferometry (2nd ed.). Amsterdam: Elsevier/Academic Press.
Heng,, I. (2009). Rotating stellar core‐collapse waveform decomposition: A principal component analysis approach. Classical and Quantum Gravity, 26(10), 105005. https://doi.org/10.1088/0264-9381/26/10/105005
Heng,, I., & Messenger,, C. (2019). Detection and classification of supernova gravitational waves signals: A deep learning approach. arXiv.Org.
Hirata,, K., Kajita,, T., Koshiba,, M., Nakahata,, M., Oyama,, Y., Sato,, N., … Cortez,, B. G. (1987). Observation of a neutrino burst from the supernova SN1987a. Physical Review Letters, 58, 1490–1493. https://doi.org/10.1103/PhysRevLett.58.1490
Hulse,, R. A., & Taylor,, J. H. (1975). Discovery of a pulsar in a binary system. The Astrophysical Journal Letters, 195, L51–L53. https://doi.org/10.1086/181708
Janka,, H.‐T. (2012). Explosion mechanisms of core‐collapse supernovae. Annual Review of Nuclear and Particle Science, 62(1), 407–451. https://doi.org/10.1146/annurev-nucl-102711-094901
Katz,, M. L., Marsat,, S., Chua,, A. J. K., Babak,, S., & Larson,, S. L. (2020). GPU‐accelerated massive black hole binary parameter estimation with LISA. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 102, 023033.
Khan,, S., Husa,, S., Hannam,, M., Ohme,, F., Pürrer,, M., Jimnez Forteza,, X., & Bohé,, A. (2016). Frequency‐domain gravitational waves from nonprecessing black‐hole binaries. II. A phenomenological model for the advanced detector era. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 93(4), 044007. https://doi.org/10.1103/PhysRevD.93.044007
Kirch,, C., Edwards,, M. C., Meier,, A., & Meyer,, R. (2019). Beyond Whittle: Nonparametric correction of a parametric likelihood with a focus on bayesian time series analysis. Bayesian Analysis, 14(4), 1037–1073.
Krolak,, A., & Schutz,, B. (1987). Coalescing binaries—Probe of the universe, 19(12), 1163–1171.
Kuroda,, T., Kotake,, K., Hayama,, K., & Takiwaki,, T. (2017). Correlated signatures of gravitational‐wave and neutrino emission in three‐dimensional general‐relativistic corecollapse supernova simulations. The Astrophysical Journal, 851(1), 62. https://doi.org/10.3847/1538-4357/aa988d
Lange,, J., O`Shaughnessy,, R., & Rizzo,, M. (2018). Rapid and accurate parameter inference for coalescing, precessing compact binaries.
LIGO Scientific Collaboration. (2018). LIGO Algorithm Library ‐ LALSuite. Free software (GPL). Retrieved from https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/index.html. doi: https://doi.org/10.7935/GT1W-FZ16
LIGO Scientific Collaboration %26 Virgo Collaboration. (2017). GRB Coordinates Network, 21513. Retrieved from https://gcn.gsfc.nasa.gov/gcn3/21513.gcn3.
LIGO Scientific Collaboration %26 Virgo Collaboration. (2020a). GraceDB — Gravitational‐Wave Candidate Event Database. Retrieved from https://gracedb.ligo.org/superevents/public/O3/.
LIGO Scientific Collaboration %26 Virgo Collaboration. (2020b). LVC Software at GWOSC. Retrieved from https://www.gw-openscience.org/software/.
Littenberg,, T., Cornish,, N., Lackeos,, K., & Robson,, T. (2020). Global analysis of the gravitational wave signal from galactic binaries. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 101, 123021.
Littenberg,, T. B., & Cornish,, N. J. (2009). Bayesian approach to the detection problem in gravitational wave astronomy. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 80, 063007. https://doi.org/10.1103/PhysRevD.80.063007
Littenberg,, T. B., & Cornish,, N. J. (2015). Bayesian inference for spectral estimation of gravitational wave detector noise. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 91(8), 084034. https://doi.org/10.1103/physrevd.91.084034
Littenberg,, T. B., Coughlin,, M., Farr,, B., & Farr,, W. (2013). Fortifying the characterization of binary mergers in ligo data. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 88(8), 084044.
Logue,, J., Ott,, C. D., Heng,, I., Kalmus,, P., & Scargill,, J. H. C. (2012). Inferring corecollapse supernova physics with gravitational waves. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 86, 044023. https://doi.org/10.1103/PhysRevD.86.044023
Lynch,, R., Vitale,, S., Essick,, R., Katsavounidis,, E., & Robinet,, F. (2017). Information‐theoretic approach to the gravitational‐wave burst detection problem. Physics Review D, 95(10), 104046. https://doi.org/10.1103/PhysRevD.95.104046
Maggiore,, M., Broeck,, C. V. D., Bartolo,, N., Belgacem,, E., Bertacca,, D., Bizouard,, M. A., … Sakellariadou,, M. (2020). Science case for the Einstein telescope. Journal of Cosmology and Astroparticle Physics, 3, 51.
Mandic,, V., Thrane,, E., Giampanis,, S., & Regimbau,, T. (2012). Parameter estimation in searches for the stochastic gravitational‐wave background. Physical Review Letters, 109, 171102. https://doi.org/10.1103/PhysRevLett.109.171102
Marsat,, S., Baker,, J. G., & Canton,, T. D. (2020). Exploring the Bayesian parameter estimation of binary black holes with LISA. arXiv.org:2003.00357v1.1–34.
Maturana‐Russel,, P., Brewer,, B. J., Klaere,, S., & Bouckaert,, R. R. (2019). Model selection and parameter inference in Phylogenetics using nested sampling. Systematic Biology, 68(2), 219–233. https://doi.org/10.1093/sysbio/syy050
Maturana‐Russel,, P., & Meyer,, R. (2019). Bayesian spectral density estimation using psplines with quantile‐based knot placement. arXiv:1905.01832.1–16.
Maturana‐Russel,, P., & Meyer,, R. (2020). Psplinepsd: P‐splines for spectral density estimation [computer software manual]. Retrieved from https://github.com/pmat747/psplinePsd.
Maturana‐Russel,, P., Meyer,, R., Veitch,, J., & Christensen,, N. (2019). Stepping‐stone sampling algorithm for calculating the evidence of gravitational wave models. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 99, 084006. https://doi.org/10.1103/PhysRevD.99.084006
Meier,, A., Kirch,, C., Edwards,, M. C., Meyer,, R., & Christensen,, N. (2018). beyondwhittle: Bayesian spectral inference for stationary time series [Computer software manual]. Retrieved from https://CRAN.R-project.org/package=beyondWhittle (R package version 0.6.0)
Metropolis,, N., Rosenbluth,, A., Rosenbluth,, M., Teller,, A., & Teller,, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.
Meyer,, R., & Christensen,, N. (2016). Gravitational waves: A statistical autopsy of a black hole merger. Significance, 13(2), 20–25. https://doi.org/10.1111/j.1740-9713.2016.00896.x
Nagar,, A., Bernuzzi,, S., del Pozzo,, W., Riemenschneider,, G., Akcay,, S., Carullo,, G., … Damour,, T. (2018). Time‐domain effective‐one‐body gravitational waveforms for coalescing compact binaries with nonprecessing spins, tides and self‐spin effects. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 98(10), 104052. https://doi.org/10.1103/PhysRevD.98.104052
Nuttall,, L. (2018). Characterizing transient noise in the ligo detectors. Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 376(2120), 20170286.
Ossokine,, S., Buonanno,, A., Marsat,, S., Cotesta,, R., Dietrich,, T., Haas,, R., … Szilágyi,, B. (2020). Multipolar effective‐one‐body waveforms for precessing binary black holes: Construction and validation. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 102, 044055.
Ott,, C. D., Burrows,, A., Livne,, E., & Walder,, R. (2004). Gravitational waves from axisym‐metric rotating stellar core collapse. The Astrophysical Journal, 600, 834–867.
Pai,, A., Dhurandhar,, S., & Bose,, S. (2001). Data‐analysis strategy for detecting gravitational‐wave signals from inspiraling compact binaries with a network of laser‐interferometric detectors. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 64(4), 30. https://doi.org/10.1103/PhysRevD.64.042004
Pankow,, C., Brady,, P., Ochsner,, E., & O`Shaughnessy,, R. (2015). Novel scheme for rapid parallel parameter estimation of gravitational waves from compact binary coalescences. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 92, 023002. https://doi.org/10.1103/PhysRevD.92.023002
Pitkin,, M., Gill,, C., Veitch,, J., Macdonald,, E., & Woan,, G. (2012). A new code for parameter estimation in searches for gravitational waves from known pulsars. Journal of Physics, 363(1), 012041.
Pitkin,, M., Isi,, M., Veitch,, J., & Woan,, G. (2017). A nested sampling code for targeted searches for continuous gravitational waves from pulsars. Physical Review Letters, 113, 151101.
Pitkin,, M., Reid,, S., Rowan,, S., & Hough,, J. (2011). Gravitational wave detection by interferometry (ground and space). Living Reviews in Relativity, 14, 5.
Powell,, J., Gossan,, S. E., Logue,, J., & Heng,, I. S. (2016). Inferring the corecollapse supernova explosion mechanism with gravitational waves. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 94, 123012. https://doi.org/10.1103/PhysRevD.94.123012
Powell,, J., Szczepanczyk,, M., & Heng,, I. S. (2017). Inferring the core‐collapse supernova explosion mechanism with three‐dimensional gravitational‐wave simulations. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 96, 123013. https://doi.org/10.1103/PhysRevD.96.123013
Pratten,, G., Husa,, S., Garcia‐Quiros,, C., Colleoni,, M., Ramos‐Buades,, A., & Estelles,, H. (2020). Setting the cornerstone for the imrphenomx family of models for gravitational waves from compact binaries: The dominant harmonic for non‐precessing quasicircular black holes. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 102, 064001.
Pürrer,, M. (2015). Frequency domain reduced order model of aligned‐spin effective‐one‐body waveforms with generic mass‐ratios and spins. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 93(6), 064041.
Robson,, T., & Cornish,, N. (2017). Impact of galactic foreground characterization on a global analysis for the LISA gravitational wave observatory. Classical and Quantum Gravity, 34(24), 244002. https://doi.org/10.1088/1361-6382/aa9601
Röver,, C., Bizouard,, M.‐A., Christensen,, N., Dimmelmeier,, H., Heng,, I., & Meyer,, R. (2009). Bayesian reconstruction of gravitational wave burst signals from simulations of rotating stellar core collapse and bounce. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 80(10), 102004. https://doi.org/10.1103/PhysRevD.80.102004
Röver,, C., Meyer,, R., & Christensen,, N. (2006). Bayesian inference on compact binary inspiral gravitational radiation signals in interferometric data. Classical and Quantum Gravity, 23, 4895–4906. https://doi.org/10.1088/0264-9381/23/15/009
Röver,, C., Meyer,, R., & Christensen,, N. (2007). Coherent bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 75(6), 062004. https://doi.org/10.1103/PhysRevD.75.062004
Roma,, V., Powell,, J., Heng,, I., & Frey,, R. (2019). Astrophysics with core‐collapse supernova gravitational wave signals in the next generation of gravitational wave detectors. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 99, 063018.
Romano,, J. D., & Cornish,, N. J. (2017). Detection methods for stochastic gravitational‐wave backgrounds: A unified treatment. Living Reviews in Relativity, 20(1), 2. https://doi.org/10.1007/s41114-017-0004-1
Romero‐Shaw,, I., Talbot,, C., Biscoveanu,, S., D`Emilio,, V., Ashton,, G., Berry,, C., … Xiao,, L. (2020). Bayesian inference for compact binary coalescences with bilby: Validation and application to the first ligo–virgo gravitational‐wave transient catalogue. arXiv.Org.
Röver,, C., Meyer,, R., & Christensen,, N. (2011). Modelling coloured residual noise in gravitational‐wave signal processing. Classical and Quantum Gravity, 28(1), 015010.
Sachdev,, S., Regimbau,, T., & Sathyaprakash,, B. (2020). Subtracting compact binary foreground sources to reveal primordial gravitational‐wave backgrounds. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 102, 024051.
Sathyaprakash,, B., Abernathy,, M., Acernese,, F., Ajith,, P., Allen,, B., Amaro‐Seoane,, P., … Bizouard,, M. (2012). Scientific objectives of einstein telescope. Classical and Quantum Gravity, 29(12), 124013.
Schutz,, B. F. (2009). A first course in general relativity (2nd ed.). Cambridge, MA: Cambridge University Press.
LIGO Scientific %26 Virgo Collaborations. (2017). The basic physics of the binary black hole merger gw150914. Annalen der Physik, 529(1–2), 1600209. https://doi.org/10.1002/andp.201600209
Setyawati,, Y., Pürrer,, M., & Ohme,, F. (2020). Regression methods in waveform modeling: A comparative study. Classical and Quantum Gravity, 37(7), 075012. https://doi.org/10.1088/1361-6382/ab693b
Shen,, H., Huerta,, E., Zhao,, Z., Jennings,, E., & Sharma,, H. (2019). Deterministic and bayesian neural networks for low‐latency gravitational wave parameter estimation of binary black hole mergers. arXiv.org, 2, 13.
Singer,, L., Chen,, H., Holz,, D., Farr,, W., Price,, L., Raymond,, V., … Mandel,, I. (2016). Going the distance: Mapping host galaxies of ligo and virgo sources in three dimensions using local cosmography and targeted follow‐up. Astrophysical Journal Letters, 829, 1–7.
Singer,, L. P., Chen,, H.‐Y., Holz,, D. E., Farr,, W. M., Price,, L. R., Raymond,, V., … Mandel,, I. (2016). Supplement: “going the distance: mapping host galaxies of ligo and virgo sources in three dimensions using local cosmography and targeted follow‐up” ( 2016, apjl, 829, l15). The Astrophysical Journal Supplement Series, 226(1), 10.
Singer,, L. P., & Price,, L. R. (2016). Rapid Bayesian position reconstruction for gravitational‐wave transients. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 93, 024013. https://doi.org/10.1103/PhysRevD.93.024013
Skilling,, J. (2006). Nested sampling for general bayesian computation. Bayesian Analysis, 1(4), 833–859. https://doi.org/10.1214/06-BA127
Smith,, R., & Ashton,, G. (2019). Expediting astrophysical discovery with gravitational‐wave transients through massively parallel nested sampling. arXiv e‐prints, arXiv:1909.11873.
Smith,, R., Field,, S. E., Blackburn,, K., Haster,, C.‐J., Pürrer,, M., Raymond,, V., & Schmidt,, P. (2016). Fast and accurate inference on gravitational waves from precessing compact binaries. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 94, 044031. https://doi.org/10.1103/PhysRevD.94.044031
Smith,, R., Talbot,, C., Vivanco,, F. H., & Thrane,, E. (2020). Inferring the population properties of binary black holes from unresolved gravitational waves. arXix:2004.09700.
Smith,, T. L., & Caldwell,, R. (2019). LISA for cosmologists: Calculating the signal‐to‐noise ratio for stochastic and deterministic sources. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 100(10), 104055. https://doi.org/10.1103/PhysRevD.100.104055
Somiya,, K. (2012). Detector configuration of kagra–the japanese cryogenic gravitational‐wave detector. Classical and Quantum Gravity, 29(12), 124007.
Stevenson,, S., Berry,, C. P. L., & Mandel,, I. (2017). Hierarchical analysis of gravitational‐wave measurements of binary black hole spin–orbit misalignments. Monthly Notices of the Royal Astronomical Society, 471(3), 2801–2811.
Stroeer,, A., Veitch,, J., Roever,, C., Bloomer,, E., Clark,, J., Christensen,, N., … Woan,, G. (2007). Inference on white dwarf binary systems using the first round mock LISA data challenges data sets. Classical and Quantum Gravity, 24, S541–S550. https://doi.org/10.1088/0264-9381/24/19/S17
Stuver,, A. (2020). LIGO Scientific Collaboration Astrophysical Sources. Retrieved from https://www.ligo.org/multimedia/gallery/ast.php.
Summerscales,, T. Z., Burrows,, A., Finn,, L. S., & Ott,, C. D. (2008). Maximum entropy for gravitational wave data analysis: Inferring the physical parameters of corecollapse supernovae. The Astrophysical Journal, 678(2), 1142–1157. https://doi.org/10.1086/528362b
Sun,, L., Goetz,, E., Kissel,, J., Betzwieser,, J., Karki,, S., Viets,, A., … Urban,, A. (2020). Characterization of systematic error in advanced ligo calibration. arXiv.Org.
Swendsen,, R. H., & Wang,, J.‐S. (1986). Replica Monte Carlo simulation of spin‐glasses. Physical Review Letters, 57(21), 2607–2609.
Talbot,, C., & Thrane,, E. (2020). Gravitational‐wave astronomy with an uncertain noise power spectral density. arXiv.Org.
Taylor,, J. H., & Weisberg,, J. M. (1982). A new test of general relativity—Gravitational radiation and the binary pulsar PSR 1913+16. The Astrophysical Journal, 253, 908–920. https://doi.org/10.1086/159690
Thrane,, E., & Talbot,, C. (2019). An introduction to bayesian inference in gravitational‐wave astronomy: Parameter estimation, model selection, and hierarchical models. Publications of the Astronomical Society of Australia, 36, e010. https://doi.org/10.1017/pasa.2019.2
Tinto,, M., & Dhurandhar,, S. V. (2014). Time‐delay interferometry. Living Reviews in Relativity, 17(1), 6. https://doi.org/10.12942/lrr-2014-6
Toubiana,, A., Marsat,, S., Babak,, S., Baker,, J., & Canton,, T. D. (2020). Parameter estimation of stellar‐mass black hole binaries with LISA.
Umstätter,, R., Christensen,, N., Hendry,, M., Meyer,, R., Simha,, V., Veitch,, J., … Woan,, G. (2005a). Bayesian modeling of source confusion in lisa data. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 72, 022001. https://doi.org/10.1103/PhysRevD.72.022001
Umstätter,, R., Christensen,, N., Hendry,, M., Meyer,, R., Simha,, V., Veitch,, J., … Woan,, G. (2005b). LISA source confusion: Identification and characterization of signals. Classical and Quantum Gravity, 22(18), S901–S911. https://doi.org/10.1088/0264-9381/22/18/s04
Umstätter,, R., Meyer,, R., Dupuis,, R. J., Veitch,, J., Woan,, G., & Christensen,, N. (2004). Estimating the parameters of gravitational waves from neutron stars using an adaptive MCMC method. Classical and Quantum Gravity, 21(20), S1655–S1665. https://doi.org/10.1088/0264-9381/21/20/008
Unnikrishnan,, C. S. (2013). IndIGO and LIGO‐India: Scope and plans for gravitational wave research and precision metrology in India. International Journal of Modern Physics D: Gravitation; Astrophysics and Cosmology, 22, 1341010. https://doi.org/10.1142/S0218271813410101
van der Sluys,, M., Raymond,, V., Mandel,, I., Röver,, C., Christensen,, N., Kalogera,, V., … Vecchio,, A. (2008). Parameter estimation of spinning binary inspirals using Markovchain Monte Carlo. Classical and Quantum Gravity, 25, 184011. https://doi.org/10.1088/0264-9381/25/18/184011
van der Sluys,, M., Röver,, C., Stroeer,, A., Christensen,, N., Kalogera,, V., Meyer,, R., & Vecchio,, A. (2008). Gravitational‐wave astronomy with Inspiral signals of spinning compact‐object binaries. The Astrophysical Journal, 688, L61. https://doi.org/10.1086/595279
Varma,, V., Field,, S., Scheel,, M., Blackman,, J., Kidder,, L., & Pfeiffer,, H. (2019). Surrogate model of hybridized numerical relativity binary black hole waveforms. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 99(6), 064045.
Veitch,, J., & Vecchio,, A. (2008a). Assigning confidence to inspiral gravitational wave candidates with bayesian model selection. Classical and Quantum Gravity, 25(18), 184010. https://doi.org/10.1088/0264-9381/25/18/184010
Veitch,, J., & Vecchio,, A. (2008b). Bayesian approach to the follow‐up of candidate gravitational wave signals. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 78, 022001. https://doi.org/10.1103/PhysRevD.78.022001
Veitch,, J., & Vecchio,, A. (2010). Bayesian coherent analysis of in‐spiral gravitational wave signals with a detector network. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 81, 062003. https://doi.org/10.1103/PhysRevD.81.062003
Veitch,, J., Raymond,, V., Farr,, B., Farr,, W. M., Graff,, P., Vitale,, S., … Wade,, L. (2015). Parameter estimation for compact binaries with ground‐based gravitational‐wave observations using the LALInference software library. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 91(4), 042003. https://doi.org/10.1103/PhysRevD.91.042003
Venumadhav,, T., Zackay,, B., Roulet,, J., Dai,, L., & Zaldarriaga,, M. (2019). A new search pipeline for compact binary mergers: Results for binary black holes in the first observing run of advanced ligo. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 100(2), 023011.
Viets,, A. D., Wade,, M., Urban,, A. L., Kandhasamy,, S., Betzwieser,, J., Brown,, D. A., … Weinstein,, A. J. (2018). Reconstructing the calibrated strain signal in the advanced ligo detectors. Classical and Quantum Gravity, 35(9), 095015.
Vinciguerra,, S., Veitch,, J., & Mandel,, I. (2017). Accelerating gravitational wave parameter estimation with multi‐band template interpolation. Classical and Quantum Gravity, 34(11), 115006. https://doi.org/10.1088/1361-6382/aa6d44
Vines,, J., Flanagan,, E. E., & Hinderer,, T. (2011). Post‐1‐newtonian tidal effects in the gravitational waveform from binary inspirals. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 83, 084051. https://doi.org/10.1103/PhysRevD.83.084051
Vinet,, J.‐Y. (2013). Some basic principles of a “LISA”. Comptes Rendus Physique, 14(4), 366–380. https://doi.org/10.1016/j.crhy.2013.02.002
Vousden,, W. D., Farr,, W. M., & Mandel,, I. (2015). Dynamic temperature selection for parallel tempering in Markov chain Monte Carlo simulations. Monthly Notices of the Royal Astronomical Society, 455(2), 1919–1937. https://doi.org/10.1093/mnras/stv2422
Welch,, P. (1967). The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70–73.
Whittle,, P. (1957). Curve and periodogram smoothing. Journal of the Royal Statistical Society: Series B: Methodological, 19(1), 38–47.
Wysocki,, D., Lange,, J., & O`Shaughnessy,, R. (2019). Reconstructing phenomenological distributions of compact binaries via gravitational wave observations. Physical Review D: Particles, Fields, Gravitation, and Cosmology, 100(4), 043012.
Zackay,, B., Dai,, L., & Venumadhav,, T. (2018). Relative binning and fast likelihood evaluation for gravitational wave parameter estimation. arXiv.Org. Retrieved from http://search.proquest.com/docview/2074056895/.
Zackay,, B., Venumadhav,, T., Roulet,, J., Dai,, L., & Zaldarriaga,, M. (2019). Detecting gravitational waves in data with non‐gaussian noise. arXiv.Org. Retrieved from http://search.proquest.com/docview/2274152872/.
Zevin,, M., Coughlin,, S., Bahaadini,, S., Besler,, E., Rohani,, N., Allen,, S., … Kalogera,, V. (2017). Gravity spy: Integrating advanced LIGO detector characterization, machine learning, and citizen science. Classical and Quantum Gravity, 34(6), 064003. https://doi.org/10.1088/1361-6382/aa5cea