EMPEROR I. 太阳系外行星MCMC并行退火用于径向速度轨道检索
EMPEROR I. Exoplanet MCMC parallel tempering for RV orbit retrieval
AI总结：
EMPEROR通过整合动态嵌套采样与自适应并行退火算法，实现高效径向速度数据中行星轨道参数的检索与模型比较，适用于多种噪声模型和不同检测场景。
摘要：
我们介绍了EMPEROR，一个开源的Python框架，旨在通过径向速度（RV）高效地检测和表征太阳系外行星。EMPEROR集成了动态嵌套采样（DNS）和自适应并行退火（APT）马尔可夫链蒙特卡洛（MCMC），支持多种噪声模型，如高斯过程（GPs）和移动平均（MA）。该框架利用统计指标进行系统性的模型比较，包括贝叶斯证据（lnZ）和贝叶斯信息准则（BIC），同时提供自动化的、可发表的可视化。EMPEROR在三个不同的系统上进行了评估，以评估其在不同检测场景中的能力。采样性能、模型选择以及地球质量行星的搜索在51 Pegasi、HD 55693和巴纳德星（GJ 699）的数据上进行了评估。对于51 Pegasi，APT在DNS上实现了3.76倍的有效采样增加，同时检索到更精确的参数估计。对于HD 55693，恢复了恒星自转周期P_rot=29.72±0.01和磁性循环周期P_mag=2557.0±70.1，同时展示了lnZ对先验选择的敏感性。对于巴纳德星，比较了多种噪声模型，并成功用所有模型检索了确认的行星参数。最佳模型显示周期为3.1536±0.0003天，最小质量为0.38±0.03 M_⊕，半长轴为0.02315±0.00039 AU。纯粹的统计推断可能不足以实现稳健的太阳系外行星检测。有效的研究方法必须整合领域知识、启发式标准和多方面的模型比较。EMPEROR在处理多样噪声结构、系统性的模型选择以及改进的性能方面具有广泛的应用价值，使其成为RV行星研究的宝贵工具。
英文摘要：
We present EMPEROR, an open-source Python framework designed for efficient exoplanet detection and characterisation with radial velocities (RV). EMPEROR integrates Dynamic Nested Sampling (DNS) and Adaptive Parallel Tempering (APT) Markov Chain Monte Carlo (MCMC), supporting multiple noise models such as Gaussian Processes (GPs) and Moving Averages (MA). The framework enables systematic model comparison using statistical metrics, including Bayesian evidence ($\ln{\mathcal{Z}}$) and Bayesian Information Criterion (BIC), while providing automated, publish-ready visualisations. EMPEROR is evaluated across three distinct systems to assess its capabilities in different detection scenarios. Sampling performance, model selection, and the search for Earth-mass planets are evaluated in data for 51 Pegasi, HD 55693 and Barnard's Star (GJ 699). For 51 Pegasi, APT achieves an effective sampling increase over DNS by a factor 3.76, while retrieving tighter parameter estimates. For HD 55693 the stellar rotation $P_{\text{rot}}=29.72^{+0.01}_{-0.02}$ and magnetic cycle $P_{\text{mag}}=2557.0^{+70.1}_{-36.7}$ are recovered, while demonstrating the sensitivity of $\ln{\mathcal{Z}}$ to prior selection. For Barnard's star, several noise models are compared, and the confirmed planet parameters are successfully retrieved with all of them. The best model shows a period of 3.1536$\pm$0.0003~d, minimum mass of 0.38$\pm$0.03 M$_{\rm{\oplus}}$, and semi-major axis of 0.02315$\pm$0.00039~AU. Purely statistical inference might be insufficient on its own for robust exoplanet detection. Effective methodologies must integrate domain knowledge, heuristic criteria, and multi-faceted model comparisons. The versatility of EMPEROR in handling diverse noise structures, its systematic model selection, and its improved performance make it a valuable tool for RV exoplanetary studies.
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。
