We find that it is possible to infer 





 and 





, at the precision of spectroscopy and relatively imprecise 





 and 





 for red-giant stars. We attempted to infer the [Fe/H]; this label is available from the apogee spectroscopy for our stars. However, this label failed and, on inspection, no pixels correlated with [Fe/H]. Therefore, contrary to the findings by Corsaro et al. (2017), we find that there is no information with respect to [Fe/H] in the granulation signal from the Kepler multiepoch photometry. We note that corrections to scaling relations between 





 and fundamental stellar parameters include both 





 and [Fe/H] (White et al. 2011; Guggenberger et al. 2016; Sharma et al. 2016). Furthermore, Viani et al. (2017) showed, in the case of 





, a dependence on mean molecular weight. While we do not find the signature in the ACF amplitude, this does indicate that a [Fe/H] dependence might be expected, as was also suggested by 3D hydrodynamical simulations of convection (Collet et al. 2007; Ludwig & Steffen 2016). For our proposed methodology, the model should be applied to test data that is assumed to be derived from the underlying population as the training data. Nevertheless, to assess the impact of stellar metallicity on the inference of our labels, we performed a test where we divided stars into two groups around the mean metallicity of the sample. We created a training set of stars with [Fe/H] > 0 and a test set with [Fe/H]  0 dex. These stars broadly cover the same 





, 





, and 





 and 





 ranges, although the means of these labels are shifted. We find that the 





 label is impacted at test time when training on a sample of stars with metallicities not covered in the test sample. The inferred 





 is biased to be about 85 K too hot, for training on the metal-rich stars and testing on the more metal-poor stars and 85 K too cold, for the reverse (although with a similar precision as before). This indicates that the [Fe/H] of the star is constraining as to the scale of the 





, even if we cannot learn this information from the data. The inference of the other three labels is not similarly affected by drawing the test and training set from different metallicity distributions. It is interesting that metallicity affects the 





 and not the other labels. This means that temperature manifests itself differently in the spectra of stars with low and high metallicities. When we do not include stars with low metallicity in the training sample, we cannot accurately predict the temperatures of low metallicity stars during test, and vice versa, as temperature-induced variations in the spectrum must depend on metallicity. The fact that this is not true for other parameters implies that variations in seismic parameters produce variations in the ACF that are independent of metallicity; we therefore do not need to know the metallicity of our stars to infer these parameters to the precision captured using this technique. There is a theoretical expectation that the asteroseismic observables will vary with stellar metallicity (e.g., White et al. 2011; Guggenberger et al. 2016; Kallinger et al. 2018; Viani et al. 2017). Following on from this, there is also opportunity to investigate this in more detail with a data-driven approach.
Citation: Ludwig & Steffen 2016
Citation function: Uses