Accurate age and mass determinations for young pre-main-sequence stars are made challenging by the presence of large-scale starspots. We present results from a near-infrared spectroscopic survey of 10 T-Tauri Stars in Taurus-Auriga that characterize spot-filling factors and temperatures, the resulting effects on temperature and luminosity determinations, and the consequences for inferred stellar masses and ages. We constructed composite models of spotted stars by combining BTSettl-CIFIST synthetic spectra of atmospheres to represent the spots and the photosphere along with continuum emission from a warm inner disk. Using a Markov Chain Monte Carlo algorithm, we find the best-fit spot and photospheric temperatures, spot-filling factors, as well as disk-filling factors. This methodology allowed us to reproduce the 0.75–2.40 μm stellar spectra and molecular feature strengths for all of our targets, disentangling the complicated multicomponent emission. For a subset of stars with multiepoch observations spanning an entire stellar rotation, we correlate the spectral variability and changes in the filling factors with rotational periods observed in K2 and AAVSO photometry. Combining spot-corrected effective temperatures and Gaia distances, we calculate luminosities and use the Stellar Parameters of Tracks with Starspots models to infer spot-corrected masses and ages for our sample of stars. Our method of accounting for spots results in an average increase of 60% in mass and a doubling in age with respect to traditional methods using optical spectra that do not account for the effect of spots.
The Effect of Starspots on Spectroscopic Age and Mass Estimates of Nonaccreting T Tauri Stars in the Taurus–Auriga Star-forming Region
Abstract
Copyright and License
© 2024. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Acknowledgement
The authors wish to thank Adolfo Carvalho for insightful mentorship and discussions regarding the implementation of several crucial steps in our analysis and Python algorithms. We also thank the referee for the thorough review and thoughtful suggestions. We thank the staff at NASA's IRTF for their support in conducting the observations that made this work possible. We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research. F.P.P. and M.M. recognize generous travel support through the Volgenau Wiley Endowed Research Fund at Colgate University. The authors wish to recognize as well the cultural significance the summit of Maunakea holds within the indigenous Hawaiian community. We have been tremendously fortunate to have had the opportunity to conduct observations from this mountain.
Facilities
IRTF (SpeX) -
Software References
astropy (Astropy Collaboration et al. 2013), Spextool (Cushing et al. 2004), matplotlib (Hunter 2007), Scipy (Virtanen et al. 2020), Numpy (van der Walt et al. 2011), lmfit (Newville et al. 2014), emcee (Foreman-Mackey et al. 2013), SpectRes (Carnall 2017), Coronagraph (Robinson et al. 2016)
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Additional details
- ISSN
- 1538-4357
- Colgate University
- Volgenau Wiley Endowed Research Fund
- Caltech groups
- Astronomy Department