Chromospheric sensitivity of stellar spectral lines: an unsupervised machine learning classification
Stellar magnetic activity alters thousands of spectral lines, limiting high-precision radial-velocity measurements, abundance analyses, and planetary characterization. We present a preliminary classification of 6403 atomic lines in synthetic visible spectra from a sequence of NLTE semi-empirical dG2 atmospheric models with increasing chromospheric heating. Principal-component analysis shows that the dominant component traces overall response amplitude, while the second distinguishes early from late responders. DBSCAN applied in the full nine-dimensional response space identifies a dense stable core and an activity- sensitive non-core group comprising about 11% of the lines. The stable core provides candidate lines for activity-insensitive measurements, while sensitive lines provide candidate activity diagnostics. Sensitive transitions tend to have low lower-level energies, although the populations overlap, making lower-level energy a statistical discriminator rather than a line-by-line predictor. A spectral sensitivity map shows that small average variations can hide nonlinear or compensating responses, demonstrating the value of the complete line-response trajectory for classification.