发表机构
Weizmann Institute of Science（魏茨曼科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低信噪比凌星行星，提出降维采样方法，将11维参数空间降至4维，快速计算边际化半径比后验，并验证其校准性及应用于Kepler数据。
AI 中文摘要
小周期长周期行星的发生率估计依赖于低信噪比凌星候选体的半径，这些候选体的后验分布应被传播到群体推断中。这些后验分布应包含所有潜在参数的误差预算，并考虑相关噪声的影响。使用高斯过程的完整马尔可夫链蒙特卡洛拟合每个目标需要数天的运行时间，并提供最终将被边际化掉的潜在参数信息。我们提出了一种方法，在仅采样较低维有效参数空间的同时，计算行星-恒星半径比的边际化后验分布。11维问题被简化为4维问题，而后验分布保留了所有干扰参数的 uncertainty 预算，包括偏心轨道、恒星参数和临边昏暗。该方法作用于白化光曲线，减轻了相关噪声引起的偏差。我们展示了在模拟系统上后验分布是校准的：真实半径具有均匀分布的 p 值。应用于419个暗弱的长周期 Kepler 感兴趣天体，该方法产生的半径比与 KOI 表大致一致，尽管它们的差异超过了报告不确定性所预期的范围。
英文摘要
Occurrence rate estimates of small long-period planets rely on the radii of low signal-to-noise ratio transit candidates whose posteriors should be propagated into the population inference. These posteriors should include the error budget from all latent parameters and account for the effects of the correlated noise. Full Markov chain Monte Carlo fits with Gaussian processes take days of runtime per target, and provide information on latent parameters that will be marginalized over anyway. We present a method that computes the marginalized posterior of the planet-to-star radius ratio while sampling only a lower-dimensional effective parameter space. The 11-dimensional problem reduces to a 4-dimensional one, while the posterior retains the uncertainty budget of all the nuisance parameters, including eccentric orbits, stellar parameters, and limb darkening. The method operates on whitened light curves, mitigating biases due to correlated noise. We show that the posteriors are calibrated on simulated systems: the true radii have uniformly distributed p-values. Applied to 419 faint long-period Kepler Objects of Interest, the method yields radius ratios broadly consistent with the KOI table, although their differences exceed those expected from the reported uncertainties.
