Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-27T09:29:32.523Z Has data issue: false hasContentIssue false
  • English
  • Français

Effects of satellite lines in fittings of He-like triplets of X-ray Spectra

Published online by Cambridge University Press:  12 October 2020

Lan Zhang Open the ORCID record for Lan Zhang [Opens in a new window] ,
Xiangxiang Xue ,
Dawei Yuan ,
Huigang Wei ,
Feilu Wang  and
Gang Zhao
Lan Zhang
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing100012, China
Xiangxiang Xue
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing100012, China School of Astronomy and Space Science University of Chinese Academy of Sciences, Beijing101408, China
Dawei Yuan
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing100012, China
Huigang Wei
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing100012, China
Feilu Wang
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing100012, China email: wfl@nao.cas.cn
Gang Zhao
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing100012, China School of Astronomy and Space Science University of Chinese Academy of Sciences, Beijing101408, China
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We estimate the wind speeds with a Bayesian inference and a Markov Chain Monte Carlo (MCMC) tool for the high resolution X-ray spectra of Vela X-1, to understand the effect of satellite lines on spectral analysis. After modelling continua and He-like triplets of the spectra with a parameterized two-component power-law model and a mullti-Gaussian model, respectively, we estimate the contamination from satellite lines, and improve the self-consistency of wind speeds derived from the He-like triplet lines of different elements. Moreover, our fitting shows that the column density of scatter component varies from phase to phase.

Keywords

methods: data analysisX-rays: binariesline: identification
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S350: Laboratory Astrophysics: From Observations to Interpretation , April 2019 , pp. 249 - 252
Copyright
© International Astronomical Union 2020

References

Bentez, N. 2000, ApJ 536, 571CrossRefGoogle Scholar
Blondin, J. M., Kallman, T. R., Fryxell, B. A., & Taam, R. E. 1990, ApJ 356, 591CrossRefGoogle Scholar
Blondin, J. M., Stevens, I. R., & Kallman, T. R. 1991, ApJ 371, 684CrossRefGoogle Scholar
Buchner, J. Georgakakis, A., Nandra, K. Hsu, L. Rangel, C., Brightman, M., Merloni, A., Salvato, M., Donley, J., & Kocevski, D. 2014, A&A 564, A125Google Scholar
Chung, H.-K., Chen, M., Morgan, W., Ralchenko, Y., & Lee, R. 2005, HEDP 1Google Scholar
Ferland, G. J., Korista, K. T., Verner, D. A., F, J. W., Kingdon, J. B., & Verner, E. M. 1998, Publ. Astron. Soc. Pac. 110, 761CrossRefGoogle Scholar
Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, Publ. Astron. Soc. Pac. 125, 306CrossRefGoogle Scholar
Fujioka, S. Takabe, H., Yamamoto, N. Salzmann, D. Wang, F., Nishimura, H., Li, Y., Dong, Q., Wang, S., Zhang, Y., Rhee, Y.-J., Lee, Y.-W., Han, J.-M., Tanabe, M., Fujiwara, T., Nakabayashi, Y., Zhao, G., Zhang, J., & Mima, K. 2009, Nature Phys., 5, 821CrossRefGoogle Scholar
Goldstein, G. Huenemoerder, D. P., & Blank, D. 2004, AJ 127, 2310CrossRefGoogle Scholar
Goodman, J., & Weare, J. 2010, Communications in Applied Mathematics and Computational Science 5, 65CrossRefGoogle Scholar
Han, B. Wang, F., Salzmann, D., & Zhao, G. 2015, 67, 29Google Scholar
MacKay, D. J. 2003, in Information theory, inference and learning algorithms (Cambridge university press)Google Scholar
Paul, B. Dewangan, G. C., Sako, M. Kahn, S. M., P, F. Liedahl, D., Wojdowski, P., & Nagase, F. 2002, in: S. Ikeuchi, J. Hearnshaw, & T. Hanawa (eds.), 8th Asian-Pacific Regional Meeting, Volume II, p. 355Google Scholar
Porquet, D., & Dubau, J. 2000, A&A 143, 495Google Scholar
Porquet, D. Dubau, J., & Grosso, N. 2010, Space Sci. Rev., 157, 103CrossRefGoogle Scholar
Press, W. H. 2007, Numerical recipes 3rd edition: The art of scientific computing (Cambridge university press)Google Scholar
Reichart, D. E., Castander, F. J., & Nichol, R. C. 1999, ApJ 516, 1CrossRefGoogle Scholar
Sako, M. Kahn, S. M., Paerels, F. Liedahl, D. A., W, S. Nagase, F., & Takahashi, T. 2003, ArXiv Astrophysics e-prints, astro-ph/0309503Google Scholar
Sako, M. Liedahl, D. A., Kahn, S. M., & Paerels, F. 1999, ApJ 525, 921CrossRefGoogle Scholar
Smith, R. K., Brickhouse, N. S., Liedahl, D. A., & Raymond, J. C. 2001, ApJL 556, L91CrossRefGoogle Scholar
Walker, M. G., Olszewski, E. W., & Mateo, M. 2015, MNRAS 448, 2717CrossRefGoogle Scholar
Wang, F. Han, B., Salzmann, D., & Zhao, G. 2017, Physics of Plasmas 24, 041403CrossRefGoogle Scholar
Watanabe, S. Sako, M., Ishida, M. Ishisaki, Y. Kahn, S. M., Kohmura, T., Nagase, F., Paerels, F., & Takahashi, T. 2006, ApJ 651, 421CrossRefGoogle Scholar
Zhang, L. Wang, F., Xue, X. Yuan, D. Wei, H. & Zhao, G. 2019, High Power Laser Science and Engineering 6, e37CrossRefGoogle Scholar