论文标题
estudio de los efectossistemáticosde sophie+ con algoritmos de aprendizajeautomático
Estudio de los efectos sistemáticos de SOPHIE+ con algoritmos de aprendizaje automático
论文作者
论文摘要
Sophie+是位于法国Haute-Provence天文台的梯形光谱仪。通过同时校准，它可以达到接近1 m s $^{ - 1} $的精度。但是，零点显示了必须校正的几个m s $^{ - 1} $的低频漂移，以实现当前系外行星搜索程序所需的精度。为此，定期监测四个径向速度标准恒星以测量仪器漂移。在这项工作中，我们提出了一种纠正Sophie+等乐器乐器漂移的新方法。我们使用监督的机器学习技术来预测以环境，器乐和观察特征作为输入的零点漂移。建立了一个具有645个观测值和120多个功能的数据集。我们探索了各种算法，并在仪器漂移的预测上实现了1.47 m s $^{ - 1} $精度。这些技术具有允许校正方法的潜力，而无需监视标准恒星，并且可以使我们了解可用于提高其稳定性和精度的仪器的知识。
SOPHIE+ is a echelle spectrograph located in Haute-Provence Observatory, France. It can reach a precision of near 1 m s$^{-1}$ by simultaneus calibration. However, the zero point shows a low frequency drift of a few m s$^{-1}$ that must be corrected to achieve the needed precision for the current exoplanet search programs. To this end, four radial velocity standard stars are monitored regularly to measure the instrumental drift. In this work, we propose a new way to correct the instrumental drift of instruments like SOPHIE+. We use supervised machine learning techniques to predict the zero point drift with environmental, instrumental and observational features as input. A dataset with 645 observations and more than 120 features was built. We explored various algorithms and achieved a precision of 1.47 m s$^{-1}$ precision on the predictions of the instrumental drift. These techniques have the potential of allowing a method of correction without the need of monitoring standard stars and also can give us knowledge about the instrument that could be used to improve its stability and precision.
