PINT: Maximum-likelihood estimation of pulsar timing noise parameters
arxiv(2024)
摘要
PINT is a pure-Python framework for high-precision pulsar timing developed on
top of widely used and well-tested Python libraries, supporting both
interactive and programmatic data analysis workflows. We present a new
frequentist framework within PINT to characterize the single-pulsar noise
processes present in pulsar timing datasets. This framework enables the
parameter estimation for both uncorrelated and correlated noise processes as
well as the model comparison between different timing and noise models. We
demonstrate the efficacy of the new framework by applying it to simulated
datasets as well as a real dataset of PSR B1855+09. We also briefly describe
the new features implemented in PINT since it was first described in the
literature.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要