AI 中文总结
该研究利用NICER数据，通过{\tt X-PSI}软件包进行脉冲轮廓建模，用轮廓似然处理干扰参数，应用频率推断确定中子星半径，精度与贝叶斯分析相当且计算更快，是频率推断确定中子星半径的原理验证，还公开了分析代码。
AI 中文摘要
近年来，NICER数据被广泛用于通过脉冲轮廓建模确定中子星半径和质量。脉冲轮廓建模通过{\tt X-PSI}软件包实现，并使用贝叶斯推断获得最佳拟合参数。利用合成数据，我们展示了使用频率推断来确定中子星半径的应用，其中干扰参数使用轮廓似然处理。我们发现轮廓似然技术能将真实半径恢复到$< 1\sigma$。其精度与贝叶斯分析相当，但计算速度更快。这项工作是使用脉冲轮廓建模通过频率推断确定中子星半径的原理验证应用，补充了所使用的贝叶斯推断技术。我们还公开了使用{\tt X-PSI}进行频率推断的分析代码。
英文摘要
In recent years, NICER data have been extensively used to determine neutron star masses and radii using pulse profile modeling. Pulse profile modeling is implemented with the {\tt X-PSI} package and the best-fit parameters are typically obtained using Bayesian inference. Using simulated data, we demonstrate the first ever application of frequentist inference to determine the neutron star radius, where the nuisance parameters are treated using profile likelihood. We find that the profile likelihood technique can recover the true radius to $< 1σ$. The uncertainty in the estimated radius is also comparable to that obtained from Bayesian analysis while being computationally much faster. Therefore, this work serves as a proof-of-principle application of frequentist inference to estimate the neutron star radius using pulse profile modeling and complements the Bayesian inference technique currently used. We have also made our analysis codes for frequentist inference using {\tt X-PSI} publicly available.
Comments10 pages, 4 figures. Accepted for publication in Universe
