Bayesian MCMC sampling of Cornell and log-modified Cornell potentials reproduces known B_c states and supplies mass predictions for higher excitations with propagated uncertainties.
Foreman-Mackey, corner.py: Scatterplot matrices in python, The Journal of Open Source Software1, 24 (2016)
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Photon counting readout detects weak postmerger gravitational wave signals at a rate of about 1 in 100 for SNR 0.2 and yields a twofold improvement in neutron star radius measurement after 20,000 events.
Simulations of PTA data show that a full gravitational-wave signal template achieves the highest Bayes factors and most robust parameter estimation for individual supermassive black hole binaries compared to an Earth-term template and a novel Spike Pixel cross-correlation model.
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$\boldsymbol{B_c}$ Meson Spectroscopy from Bayesian MCMC: Probing Confinement and State Mixing
Bayesian MCMC sampling of Cornell and log-modified Cornell potentials reproduces known B_c states and supplies mass predictions for higher excitations with propagated uncertainties.
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Photon counting readout for detection and inference of gravitational waves from neutron star merger remnants
Photon counting readout detects weak postmerger gravitational wave signals at a rate of about 1 in 100 for SNR 0.2 and yields a twofold improvement in neutron star radius measurement after 20,000 events.
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Expectations for the first supermassive black-hole binary resolved by PTAs I: Model efficacy
Simulations of PTA data show that a full gravitational-wave signal template achieves the highest Bayes factors and most robust parameter estimation for individual supermassive black hole binaries compared to an Earth-term template and a novel Spike Pixel cross-correlation model.