A comparison of stellar atmospheric parameters from the LAMOST and APOGEE datasets

We have compared stellar parameters, including temperature, gravity and metallicity, for common stars in the LAMOST DR2 and SDSS DR12/APOGEE datasets. It is found that the LAMOST dataset provides a more well-defined red clump feature than the APOGEE dataset in the T-eff versus log g diagram. With this advantage, we have separated red clump stars from red giant stars, and attempt to establish calibrations between the two datasets for the two groups of stars. The results show that there is a good consistency in temperature with a calibration close to the one-to-one line, and we can establish a satisfactory metallicity calibration of [Fe/H](APOGEE) = 1.18 [Fe/H](LAMOST) + 0.11 with a scatter of similar to 0.08 dex for both the red clump and red giant branch samples. For gravity, there is no correlation for red clump stars between the two datasets, and scatters around the calibrations of red giant stars are substantial. We found two main sources of scatter in log g for red giant stars. One is a group of stars with 0.00253x T-eff -8.67 < log g < 2.6 located in the forbidden region, and the other is the contaminated red clump stars, which could be picked out from the unmatched region where stellar metallicity is not consistent with position in the T-eff versus log g diagram. After excluding stars in these two regions, we have established two calibrations for red giant stars, log g(APOGEE) = 0.000615 x T-eff,T- LAMOST + 0.697 x log g(LAMOST) - 2.208 (sigma = 0. 1 5 0) for [Fe/H] > -1 and log g(APOGEE) = 0.000874 x T-eff,T-LAMOST + 0.588 x log g(LAMOST) - 3.117 (sigma = 0.167) for [Fe/ H] < -1. The calibrations are valid for stars with T-eff = 3800 - 5400 K and log g = 0 - 3.8 dex, and are useful in work aiming to combine the LAMOST and APOGEE datasets in a future study. In addition, we find that an SVM method based on asteroseismic log g is a good way to greatly improve the accuracy of gravity for these two regions, at least in the LAMOST dataset.