论文标题
自然与培养:研究测量不确定性在评估行星和恒星特性之间的潜在趋势中的影响
Nature vs. Nurture: Investigating the Effects of Measurement Uncertainties in the Assessment of Potential Trends Between Planetary and Stellar Properties
论文作者
论文摘要
行星和恒星特性(尤其是年龄)之间的相关性可以提供对行星形成和进化过程的见解。但是,这种趋势的潜在来源可能不清楚,测量不确定性和小样本量可能会对观察到的趋势是否真正存在留下怀疑。我们使用贝叶斯框架来检查测量参数的不确定性如何影响观察到的趋势来源的竞争假设的几率。我们分析了文献报道的三个趋势。在每种应用中,尽管不确定性确实会影响不确定性的数值,但我们的结论仍然相同,无论是否考虑到不确定性:热木星偏心率会随着时间的流逝而循环,热木星宿主的倾斜驱动是由最高的温度驱动的,并且有足够的证据来促进2:1:1:1个天气相关的趋势,偏爱年龄的趋势。 2:1的谐振和倾斜案例的更新样本不会改变原始结论。模拟2:1共振数据表明,样本量可能比得出企业结论的测量精度更重要。但是,如果2:1的共振在各个范围的时间尺度上被破坏,那么即使使用大型样本,年龄趋势也很难在偶然的关系上确认。对于某些应用,完全合并测量不确定性在计算上可能太昂贵了,因此最好使用框架而无需不确定性并执行其他测试来检查高度不确定的测量结果。
Correlations between planetary and stellar properties, particularly age, can provide insight on planetary formation and evolution processes. However, the underlying source of such trends can be unclear, and measurement uncertainties and small sample sizes can leave doubt as to whether an observed trend truly exists. We use a Bayesian framework to examine how uncertainties in measured parameters influence the odds ratios of competing hypotheses for the source of an observed trend. We analyze three reported trends from the literature. In each application, while uncertainties do affect the numerical value of the odds ratios, our conclusions remain the same whether or not uncertainties are taken into account: hot Jupiter eccentricities are circularized over time, obliquities of hot Jupiter hosts are driven by stellar temperature, and there is not enough evidence to favor a trend of 2:1 orbital resonances with age over a chance relation. Updated samples for the 2:1 resonances and obliquities cases do not change the original conclusions. Simulated 2:1 resonance data show that sample size may be more important than measurement precision for drawing a firm conclusion. However, if 2:1 resonances get disrupted on a wide range of timescales, an age trend will be inherently difficult to confirm over a chance relation, even with a large sample. For some applications, full incorporation of measurement uncertainties may be too computationally expensive, making it preferable to use the framework without uncertainties and perform additional tests to examine the effects of highly uncertain measurements.