论文标题
一种使用有意义的先验信息检测太阳能振荡的概率方法
A probabilistic method for detecting solar-like oscillations using meaningful prior information
论文作者
论文摘要
当前和未来的基于空间的观测值，例如过渡系外行星调查卫星（TESS）和柏拉图，可以为振荡恒星提供大量的新数据，尤其是类似于太阳的振荡的恒星。太阳样振荡器构成了大多数已知的振荡恒星，因此自动分析方法正成为尽可能多地使用这些数据的越来越多的必要性。在这里，我们旨在构建一种算法，该算法可以自动确定给定时间序列的光度测量结果是否显示出太阳样振荡的证据。该算法旨在分析苔丝任务和未来柏拉图任务的数据，尤其是在主要和次级进化阶段的明星。该算法首先测试苔丝光曲线功率光谱中可观察到的频率的范围，这与太阳能振荡的预期相一致。另外，算法测试是否在时间序列中存在振荡频率的重复模式，以及它是否与太阳样振荡器中看到的大分离一致。两种方法都使用缩放关系和观察结果，这些观察结果是在Corot，Kepler和K2任务中建立和获得的。使用一组测试数据，该数据由苔丝观察到的视觉确认的太阳能振荡器和非振荡器组成，我们发现所提出的算法可以达到$ 94.7 \％$ true true的正率和$ 8.2 \％$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $。但是，通过采用更严格的选择标准，可以将误报利率降低至$ \ of2 \％$，同时保留$ 80 \％$ $ true的正利率。
Current and future space-based observatories such as the Transiting Exoplanet Survey Satellite (TESS) and PLATO are set to provide an enormous amount of new data on oscillating stars, and in particular stars that oscillate similar to the Sun. Solar-like oscillators constitute the majority of known oscillating stars and so automated analysis methods are becoming an ever increasing necessity to make as much use of these data as possible. Here we aim to construct an algorithm that can automatically determine if a given time series of photometric measurements shows evidence of solar-like oscillations. The algorithm is aimed at analyzing data from the TESS mission and the future PLATO mission, and in particular stars in the main-sequence and subgiant evolutionary stages. The algorithm first tests the range of observable frequencies in the power spectrum of a TESS light curve for an excess that is consistent with that expected from solar-like oscillations. In addition, the algorithm tests if a repeating pattern of oscillation frequencies is present in the time series, and whether it is consistent with the large separation seen in solar-like oscillators. Both methods use scaling relations and observations which were established and obtained during the CoRoT, Kepler, and K2 missions. Using a set of test data consisting of visually confirmed solar-like oscillators and nonoscillators observed by TESS, we find that the proposed algorithm can attain a $94.7\%$ true positive rate and a $8.2\%$ false positive rate at peak accuracy. However, by applying stricter selection criteria, the false positive rate can be reduced to $\approx2\%$, while retaining an $80\%$ true positive rate.
