利用模板拟合与随机森林分类从暗能量巡天的多波段稀疏采样数据中识别 RR Lyrae 星
太阳与恒星天体物理
2019-06-26 v1 星系天体物理
摘要
许多研究表明，RR Lyrae 变星（RRL）是银河系晕结构及卫星星系的强有力恒星示踪物。暗能量巡天（DES）具有深且宽的覆盖（单次曝光 g ~ 23.5 mag；超过 5000 deg），为搜寻至银河系晕边缘的亚结构提供了丰富机会。然而，DES 广域巡天的稀疏且不均匀采样的多波段光变曲线（前三年在 grizY 各波段中位数为 4 次观测）对传统用于探测 RRL 的技术提出了挑战。我们提出了一种基于经验驱动且计算高效的模板拟合方法，利用三年 DES 数据识别这些变星。当在 SDSS stripe 82 中先前已分类天体的 DES 光变曲线上测试时，我们的算法以 85% 纯度和 76% 完备率将 89% 的 RRL 周期恢复到其真值的 1% 以内。利用该方法，我们识别出 5783 个 RRL 候选体，其中约 31% 为先前未发现。该方法将有助于在其他稀疏多波段数据集中识别 RRL。
引用
@article{arxiv.1905.00428,
title = {Identification of RR Lyrae stars in multiband, sparsely-sampled data from the Dark Energy Survey using template fitting and Random Forest classification},
author = {K. M. Stringer and J. P. Long and L. M. Macri and J. L. Marshall and A. Drlica-Wagner and C. E. Martínez-Vázquez and A. K. Vivas and K. Bechtol and E. Morganson and M. Carrasco Kind and A. B. Pace and A. R. Walker and C. Nielsen and T. S. Li and E. Rykoff and D. Burke and A. Carnero Rosell and E. Neilsen and P. Ferguson and S. A. Cantu and J. L. Myron and L. Strigari and A. Farahi and F. Paz-Chinchón and D. Tucker and Z. Lin and D. Hatt and J. F. Maner and L. Plybon and A. H. Riley and E. O. Nadler and T. M. C. Abbott and S. Allam and J. Annis and E. Bertin and D. Brooks and E. Buckley-Geer and J. Carretero and C. E. Cunha and C. B. D'Andrea and L. N. da Costa and J. De Vicente and S. Desai and P. Doel and T. F. Eifler and B. Flaugher and J. Frieman and J. García-Bellido and E. Gaztanaga and D. Gruen and J. Gschwend and G. Gutierrez and W. G. Hartley and D. L. Hollowood and B. Hoyle and D. J. James and K. Kuehn and N. Kuropatkin and P. Melchior and R. Miquel and R. L. C. Ogando and A. A. Plazas and E. Sanchez and B. Santiago and V. Scarpine and M. Schubnell and S. Serrano and I. Sevilla-Noarbe and M. Smith and R. C. Smith and M. Soares-Santos and F. Sobreira and E. Suchyta and M. E. C. Swanson and G. Tarle and D. Thomas and V. Vikram and B. Yanny},
journal= {arXiv preprint arXiv:1905.00428},
year = {2019}
}
备注
34 pages, 21 figures. Accepted for publication in AJ. Data products are available at https://des.ncsa.illinois.edu/releases/other/y3-rrl