arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2008.01186astro-ph.SR
收藏

M矮星系外行星的CARMENES搜索--确定目标恒星基本参数的深度学习方法

The CARMENES search for exoplanets around M dwarfs -- A deep learning approach to determine fundamental parameters of target stars

V. M. Passegger, A. Bello-García, J. Ordieres-Meré, J. A. Caballero, A. Schweitzer, A. González-Marcos, I. Ribas, A. Reiners, A. Quirrenbach, P. J. Amado, M. Az… 展开作者

V. M. Passegger, A. Bello-García, J. Ordieres-Meré, J. A. Caballero, A. Schweitzer, A. González-Marcos, I. Ribas, A. Reiners, A. Quirrenbach, P. J. Amado, M. Azzaro, F. F. Bauer, V. J. S. Béjar, M. Cortés-Contreras, S. Dreizler, A. P. Hatzes, Th. Henning, S. V. Jeffers, A. Kaminski, M. Kürster, M. Lafarga, E. Marfil, D. Montes, J. C. Morales, E. Nagel, L. M. Sarro, E. Solano, H. M. Tabernero, M. Zechmeister

摘要:

现有的和即将到来的仪器正在收集大量的天体物理数据,这需要高效和快速的分析技术。我们提出了一个深度神经网络架构来分析高分辨率恒星光谱,并预测恒星参数,如有效温度,表面重力,金属丰度和旋转速度。通过这项研究,我们首先证明了深度神经网络从合成训练集中精确恢复恒星参数的能力。其次,我们分析了这种方法在观测光谱中的应用以及合成间隙(即,观测光谱和合成光谱之间的差异)对恒星参数估计、误差和精度的影响。我们的卷积网络在不同的光学和近红外波长区域的合成PHOENIX-ACES光谱上进行训练。对于四个恒星参数$T_{\rm eff}$、$\log{g}$、[M/H]和$v \sin{i}$中的每一个,我们构建了一个神经网络模型来独立地估计每个参数。然后,我们将这种方法应用于50 M矮星与CARMENES(Calar Alto high-Resolution search for M dwarfs with Exo-earths with Near-infrared and optical Echelle Spectrographs)拍摄的高分辨率光谱,该光谱同时在可见光(520-960 nm)和近红外波长范围(960-1710 nm)工作。我们的结果进行了比较,这些恒星的文献值。它们在误差范围内显示出很好的一致性,但在某些情况下也表现出很大的偏差,特别是对于[M/H],这指出了更好地了解合成差距的重要性。

英文摘要:

Existing and upcoming instrumentation is collecting large amounts of astrophysical data, which require efficient and fast analysis techniques. We present a deep neural network architecture to analyze high-resolution stellar spectra and predict stellar parameters such as effective temperature, surface gravity, metallicity, and rotational velocity. With this study, we firstly demonstrate the capability of deep neural networks to precisely recover stellar parameters from a synthetic training set. Secondly, we analyze the application of this method to observed spectra and the impact of the synthetic gap (i.e., the difference between observed and synthetic spectra) on the estimation of stellar parameters, their errors, and their precision. Our convolutional network is trained on synthetic PHOENIX-ACES spectra in different optical and near-infrared wavelength regions. For each of the four stellar parameters, $T_{\rm eff}$, $\log{g}$, [M/H], and $v \sin{i}$, we constructed a neural network model to estimate each parameter independently. We then applied this method to 50 M dwarfs with high-resolution spectra taken with CARMENES (Calar Alto high-Resolution search for M dwarfs with Exo-earths with Near-infrared and optical Echelle Spectrographs), which operates in the visible (520-960 nm) and near-infrared wavelength range (960-1710 nm) simultaneously. Our results are compared with literature values for these stars. They show mostly good agreement within the errors, but also exhibit large deviations in some cases, especially for [M/H], pointing out the importance of a better understanding of the synthetic gap.

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。

↑