%0 Generic %1 gilhool2019datadriven %A Gilhool, Steven %A Blake, Cullen %D 2019 %K Methods %T A Data-Driven Technique for Measuring Stellar Rotation %U http://arxiv.org/abs/1902.11182 %X Measuring stellar rotational velocities is a powerful way to probe the many astrophysical phenomena that drive, or are driven by, the evolution of stellar angular momentum. In this paper, we present a novel data-driven approach to measuring the projected rotational velocity, $vi$. Rather than directly measuring the broadening of spectral lines, we leverage the large information content of high-resolution spectral data to empirically estimate $vi$. We adapt the framework laid down by The Cannon (Ness et al. 2015), which trains a generative model of the stellar flux as a function of wavelength using high-fidelity reference data, and can then produce estimates of stellar parameters and abundances for other stars directly from their spectra. Instead of modeling the flux as a function of wavelength, however, we model the first derivative of the spectra, as we expect the slopes of spectral lines to change as a function of $vi$. This technique is computationally efficient and provides a means of rapidly estimating $vi~$ for large numbers of stars in spectroscopic survey data. We analyze SDSS APOGEE spectra, constructing a model informed by high-fidelity stellar parameter estimates derived from high-resolution California Kepler Survey spectra of the same stars. We use the model to estimate $vi~$ up to $15\,km\,s^-1$ for $27,000$ APOGEE spectra, in fractions of a second per spectrum. Our estimates agree with the APOGEE $vi~$ estimates to within $1.2\,km\,s^-1$.

@misc{gilhool2019datadriven, abstract = {Measuring stellar rotational velocities is a powerful way to probe the many astrophysical phenomena that drive, or are driven by, the evolution of stellar angular momentum. In this paper, we present a novel data-driven approach to measuring the projected rotational velocity, $v\sin{i}$. Rather than directly measuring the broadening of spectral lines, we leverage the large information content of high-resolution spectral data to empirically estimate $v\sin{i}$. We adapt the framework laid down by The Cannon (Ness et al. 2015), which trains a generative model of the stellar flux as a function of wavelength using high-fidelity reference data, and can then produce estimates of stellar parameters and abundances for other stars directly from their spectra. Instead of modeling the flux as a function of wavelength, however, we model the first derivative of the spectra, as we expect the slopes of spectral lines to change as a function of $v\sin{i}$. This technique is computationally efficient and provides a means of rapidly estimating $v\sin{i}~$ for large numbers of stars in spectroscopic survey data. We analyze SDSS APOGEE spectra, constructing a model informed by high-fidelity stellar parameter estimates derived from high-resolution California Kepler Survey spectra of the same stars. We use the model to estimate $v\sin{i}~$ up to $15\,km\,s^{-1}$ for $27,000$ APOGEE spectra, in fractions of a second per spectrum. Our estimates agree with the APOGEE $v\sin{i}~$ estimates to within $1.2\,km\,s^{-1}$.}, added-at = {2019-03-01T15:41:37.000+0100}, author = {Gilhool, Steven and Blake, Cullen}, biburl = {https://www.bibsonomy.org/bibtex/2a5438ed7c7687370700d5a6ddeaf4e45/superjenwinters}, description = {A Data-Driven Technique for Measuring Stellar Rotation}, interhash = {1130ba8031cff686511fb10d04134c48}, intrahash = {a5438ed7c7687370700d5a6ddeaf4e45}, keywords = {Methods}, note = {cite arxiv:1902.11182Comment: 26 pages, 10 figures. Accepted for publication in ApJ}, timestamp = {2019-03-01T15:41:37.000+0100}, title = {A Data-Driven Technique for Measuring Stellar Rotation}, url = {http://arxiv.org/abs/1902.11182}, year = 2019 }