arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.09388astro-ph.HE
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基于隐马尔可夫模型的脉冲星毛刺检测

Pulsar glitch detection with a hidden Markov model

A. Melatos, L. M. Dunn, S. Suvorova, W. Moran, R. J. Evans

摘要:

脉冲星定时实验通常通过绝对脉冲编号从到达时间(TOA)序列生成相位连接定时解决方案,即通过在TOA之间拟合整数个脉冲以最小化相对于参数化相位模型的残差。在这种观测模式下,旋转毛刺被发现,当无毛刺相位模型的残差在某个时期后发散时,毛刺参数通过贝叶斯跟踪进行细化。这里提出了一种替代的补充方法,其利用隐马尔可夫模型(HMM)跟踪脉冲频率$f$及其时间导数$df/dt$,其动态包括随机自旋漂移(定时噪声)和$f$和$df/dt$中的脉冲跳跃(毛刺)。HMM明确地跟踪自旋漂移,作为离散时间马尔可夫链的具体实现。它通过比较故障和无故障模型的贝叶斯因子来发现故障。为了方便起见,它采用标准TOA,并且完全自动化,允许通过蒙特卡罗模拟快速计算性能界限。实际的,面向用户的图呈现的虚警概率和检测阈值(例如,最小可分辨的毛刺大小)与观测调度参数(例如,TOA的不确定性,TOA之间的平均延迟)和毛刺参数(例如,瞬态和永久跳跃大小,指数恢复时间尺度)。HMM也适用于PSR J 0835 -4510中包含2016年12月12日故障的$\sim 1$年实际数据,作为原理证明。它检测已知的毛刺,并确认大小为$> 10^{-7} f$的相同数据中不存在其他毛刺。

英文摘要:

Pulsar timing experiments typically generate a phase-connected timing solution from a sequence of times-of-arrival (TOAs) by absolute pulse numbering, i.e. by fitting an integer number of pulses between TOAs in order to minimize the residuals with respect to a parametrized phase model. In this observing mode, rotational glitches are discovered, when the residuals of the no-glitch phase model diverge after some epoch, and glitch parameters are refined by Bayesian follow-up. Here an alternative, complementary approach is presented which tracks the pulse frequency $f$ and its time derivative $df/dt$ with a hidden Markov model (HMM), whose dynamics include stochastic spin wandering (timing noise) and impulsive jumps in $f$ and $df/dt$ (glitches). The HMM tracks spin wandering explicitly, as a specific realization of a discrete-time Markov chain. It discovers glitches by comparing the Bayes factor for glitch and no-glitch models. It ingests standard TOAs for convenience and, being fully automated, allows performance bounds to be calculated quickly via Monte Carlo simulations. Practical, user-oriented plots are presented of the false alarm probability and detection threshold (e.g. minimum resolvable glitch size) versus observational scheduling parameters (e.g. TOA uncertainty, mean delay between TOAs) and glitch parameters (e.g. transient and permanent jump sizes, exponential recovery time-scale). The HMM is also applied to $\sim 1$ yr of real data bracketing the 2016 December 12 glitch in PSR J0835-4510 as a proof of principle. It detects the known glitch and confirms that no other glitch exists in the same data with size $> 10^{-7} f$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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