Title: Reconstructing Intrinsic Stellar Noise with Stellar Atmospheric Parameters and Chromospheric Activity

Abstract: Accurately characterizing intrinsic stellar photometric noise induced by stellar astrophysics, such as stellar activity, granulation, and oscillations, is of crucial importance for detecting transiting exoplanets. In this study, we investigate the relation between the intrinsic stellar photometric noise, as quantified by the Kepler rrmsCDPP measurement, and the level of stellar chromospheric activity, as indicated by the S-index of Ca II HK lines derived from the LAMOST spectra. Our results reveal a clear positive correlation between S-index and rrmsCDPP, and the correlation becomes more significant at higher activity levels and on longer timescales. We have therefore built an empirical relation between rrmsCDPP and S-index as well as Teff, logg, [Fe/H], and apparent magnitude with the XGBoost regression algorithm, using the LAMOST-Kepler common star sample as the training set. This method achieves a precision of ~20 ppm for inferring the intrinsic noise from the S-index and other stellar labels on a 6-hour integration duration. We have applied this empirical relation to the full LAMOST DR7 spectra database, and obtained the intrinsic noise predictions for 1,358,275 stars. The resultant catalog is publicly available and expected to be valuable for optimizing target selection for future exoplanet-hunting space missions, such as the Earth 2.0 mission.