Uncertainty Quantification of Stochastic Computer Model for Binary Black Hole Formation

• Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
• Type: Invited
• Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
• Sponsor: Astrostatistics Special Interest Group
• Author(s): Luyao Lin* and Jim Barrett and Derek Bingham and Ilya Mandel
• Companies: Simon Fraser University and University of Birmingham and Simon Fraser University and University of Birmingham
• Keywords: Uncertainty Quantification; Gaussian Process; Computer Experiments; Model Calibration; Binary Black Holes; Chirp Mass

Abstract

Computer experiments are often used to simulate complex physical systems to gain insights of their behaviour. We aim to investigate the formation of binary black holes by means of computer models, with high dimensional input that consists of both random initial conditions and physical parameters. Knowledge of the physical parameters can greatly improve the efficiency of the computer experiment, as the success rate of producing binary black holes characterized by a chirp mass with randomly drawn parameters is extremely low. By combining field data with Gaussian process models as surrogates for the computer model, the physical parameters are calibrated towards their true value. We can accurately predict the distribution of binary black hole chirp mass, at a small fraction of the computational cost of the original models.