Guilt by Association: Finding Cosmic Ray Sources Using Hierarchical Bayesian Clustering

Soiaporn, Kunlaya; Chernoff, David F.; Loredo, Thomas J.; Ruppert, David; Wasserman, Ira

doi:10.1007/978-1-4614-3520-4_64

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…(17). In principle, one would want to be more careful and consider tidal effects and mass transfer [46] as possible contributions to _ f. However, it is expected 6 (color online).…”

Section: Chirp Mass Priormentioning

confidence: 99%

“…The method is general and can be applied to any number of astrophysical model selection problems [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…We develop a simple yet comprehensive Hierarchical Bayesian modeling approach that uses the full multidimensional and highly correlated parameter uncertainties of a collection of signals to constrain the joint parameter distributions of the underlying astrophysical models. The method is general and can be applied to any number of astrophysical model selection problems [10][11][12] A remarkable feature of the Hierachial Bayesian method is that in its purest form it is completely free of selection effects such as Malmquist bias. By "purest form" we mean where the signal model extends over the entire source population, including those with vanishingly small signal-to-noise ratio [13].…”

Section: Introductionmentioning

confidence: 99%

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Astrophysical model selection in gravitational wave astronomy

2012

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Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve $5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.

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“…(17). In principle, one would want to be more careful and consider tidal effects and mass transfer [46] as possible contributions to _ f. However, it is expected 6 (color online).…”

Section: Chirp Mass Priormentioning

confidence: 99%

“…The method is general and can be applied to any number of astrophysical model selection problems [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Astrophysical model selection in gravitational wave astronomy

2012

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Coincident Detection Significance in Multimessenger Astronomy

Ashton

Burns

Canton

et al. 2018

ApJ

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We derive a Bayesian criterion for assessing whether signals observed in two separate data sets originate from a common source. The Bayes factor for a common vs. unrelated origin of signals includes an overlap integral of the posterior distributions over the common source parameters. Focusing on multimessenger gravitational-wave astronomy, we apply the method to the spatial and temporal association of independent gravitational-wave and electromagnetic (or neutrino) observations. As an example, we consider the coincidence between the recently discovered gravitational-wave signal GW170817 from a binary neutron star merger and the gamma-ray burst GRB 170817A: we find that the common source model is enormously favored over a model describing them as unrelated signals.

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Guilt by Association: Finding Cosmic Ray Sources Using Hierarchical Bayesian Clustering

Cited by 2 publications

References 4 publications

Astrophysical model selection in gravitational wave astronomy

Astrophysical model selection in gravitational wave astronomy

Coincident Detection Significance in Multimessenger Astronomy

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