标题： 您的通勤中有什么？ 迈向从系外行星凌日数据中更可靠地获取$5\times$科学的途径 显示英文标题

标题： What's in Your Transit? Towards Reliably Getting $5\times$ More Science from Exoplanet Transit Data

摘要： 系外行星科学高度依赖于掩食深度（$D$）的测量。 然而，随着仪器精度的提高，$D$的不确定性似乎越来越偏离仅由光子噪声驱动的预期。 在这里，我们通过定义一个放大因子，$A$，来表征这一不足（掩食深度精度问题，TDPP），该因子量化了在相同时间窗口大小下测得的掩食深度不确定性与测得的基线散射之间的差异。 虽然理论上$A$应该是$\sim\sqrt{3}$，但我们发现由于$D$与边缘暗淡系数（LDCs）之间的相关性，其值可以达到$\gtrsim$10。 这意味着（1）通过LDC的可靠先验，基于交通的系外行星研究（例如大气研究）的性能可以得到显著提升，以及（2）对LDC的低保真先验可能导致$D$的显著偏差——可能由于这些偏差的波长依赖性而影响大气研究。 出于同样的原因，恒星密度和行星形状/边缘不对称性的测量也可能出现偏差。 在当前的光度精度下，我们建议使用3$^{\rm rd}$阶多项式定律和4$^{\rm th}$阶非线性定律，因为它们在偏差和$A$之间提供了最佳折中方案，同时测试每种参数化的保真度。 虽然它们与现有的LDC先验（10-20%的不确定性）结合使用目前意味着$A\sim10$，但我们表明，针对边缘变暗模型的针对性改进可以将$A$降低到$\sim2$。 改进恒星模型和凌日拟合方法对于充分利用凌日数据集至关重要，通过$5\times$可靠地提高其科学产出，从而使得用最多$25\times$次更少的凌日即可实现相同科学目标。 显示英文摘要

摘要： Exoplanetary science heavily relies on transit depth ($D$) measurements. Yet, as instrumental precision increases, the uncertainty on $D$ appears to increasingly drift from expectations driven solely by photon-noise. Here we characterize this shortfall (the Transit-Depth Precision Problem, TDPP), by defining an amplification factor, $A$, quantifying the discrepancy between the measured transit-depth uncertainty and the measured baseline scatter on a same time bin size. While in theory $A$ should be $\sim\sqrt{3}$, we find that it can reach values $\gtrsim$10 notably due to correlations between $D$ and the limb-darkening coefficients (LDCs). This means that (1) the performance of transit-based exoplanet studies (e.g., atmospheric studies) can be substantially improved with reliable priors on LDCs and (2) low-fidelity priors on the LDCs can yield substantial biases on $D$--potentially affecting atmospheric studies due to the wavelength-dependence of such biases. For the same reason, biases may emerge on stellar-density and planet-shape/limb-asymmetry measurements. With current photometric precisions, we recommend using a 3$^{\rm rd}$-order polynomial law and a 4$^{\rm th}$-order non-linear law, as they provide an optimal compromise between bias and $A$, while testing the fidelity for each parametrization. While their use combined with existing LDC priors (10-20% uncertainty) currently implies $A\sim10$, we show that targeted improvements to limb-darkening models can bring $A$ down to $\sim2$. Improving stellar models and transit-fitting practices is thus essential to fully exploit transit datasets, and reliably increasing their scientific yield by $5\times$, thereby enabling the same science with up to $25\times$ fewer transits.