UNCERTAINTY QUANTIFICATION OF A COMPUTER MODEL FOR BINARY BLACK HOLE FORMATION

• 成果类型: Article
• 署名作者: Lin, Luyao; Bingham, Derek; Broekgaarden, Floor; Mandel, Ilya
• 署名单位: Simon Fraser University; Smithsonian Astrophysical Observatory; Smithsonian Institution; Harvard University; Monash University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1484
• 发表日期: 2021
• 页码: 1604-1627
• 关键词: evolution

摘要

In this paper, a fast and parallelizable method based on Gaussian processes (GPs) is introduced to emulate computer models that simulate the formation of binary black holes (BBHs) through the evolution of pairs of massive stars. Two obstacles that arise in this application are the a priori unknown conditions of BBH formation and the large scale of the simulation data. We address them by proposing a local emulator which combines a GP classifier and a GP regression model. The resulting emulator can also be utilized in planning future computer simulations through a proposed criterion for sequential design. By propagating uncertainties of simulation input through the emulator, we are able to obtain the distribution of BBH properties under the distribution of physical parameters.