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arXiv 0902.2014astro-ph.EP
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用贝叶斯混合MCMC开普勒周期图探测太阳系外行星

Detecting Extra-solar Planets with a Bayesian hybrid MCMC Kepler periodogram

P. C. Gregory

摘要:

对已公布的径向速度数据集进行的贝叶斯重新分析为其他行星候选者提供了证据。非线性模型拟合是用一种新的混合马尔可夫链蒙特卡罗(HMCMC)算法,它结合了并行回火,模拟退火和遗传交叉操作。这些特征中的每一个都有助于检测卡方面积中的全局最小值。通过将这三者结合起来,HMCMC大大增加了实现这一目标的可能性。当应用到开普勒问题,它作为一个强大的多行星开普勒周期图参数估计和模型选择。HMCMC算法嵌入在一个独特的两阶段自适应控制系统,自动调整的MCMC建议分布通过退火操作。

英文摘要:

A Bayesian re-analysis of published radial velocity data sets is providing evidence for additional planetary candidates. The nonlinear model fitting is accomplished with a new hybrid Markov chain Monte Carlo (HMCMC) algorithm which incorporates parallel tempering, simulated annealing and genetic crossover operations. Each of these features facilitate the detection of a global minimum in chi^2. By combining all three, the HMCMC greatly increases the probability of realizing this goal. When applied to the Kepler problem it acts as a powerful multi-planet Kepler periodogram for both parameter estimation and model selection. The HMCMC algorithm is embedded in a unique two stage adaptive control system that automates the tuning of the MCMC proposal distributions through an annealing operation.

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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