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Bayesian Rate Estimation for FRBs (barb)

Presentation #152.06 in the session “Models and Simulations in High-Energy Regimes”.

Published onJan 11, 2021
Bayesian Rate Estimation for FRBs (barb)

Using the framework from Lawrence et. al 2017, we employed Markov Chain Monte Carlo to estimate the rate of FRBs. The framework uses flux of the FRB, sensitivity at full width half-maximum (FWHM), FWHM diameter, number of beams, and time per beam to calculate a reasonable rate. In addition to calculating the rate of FRB events this python package is also integrating methods to rigorously model spectral index limits for FRBs. There are other estimations of FRB rates, but Markov Chain Monte Carlo is a particularly robust because it is possible to integrate over several unknowns and allows us to use a maximum likelihood approach to estimate the model. These questions are still in the early phases of being investigated, so having a statistically sound program that is user-friendly will advance communal knowledge of this developing field.


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