Chromospheric sensitivity of stellar spectral lines: an unsupervised machine learning classification
Abstract
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.
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BibTeX
@misc{peralta2026chromospheric,
title = {{Chromospheric sensitivity of stellar spectral lines: an unsupervised machine learning classification}},
author = {Juan I. Peralta and Mariela C. Vieytes},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.22532},
eprint = {2609.22532},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.22532},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Chromospheric sensitivity of stellar spectral lines: an unsupervised machine learning classification
AU - Juan I. Peralta
AU - Mariela C. Vieytes
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.22532
UR - https://arxiv.org/abs/2609.22532
AB - 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.
ER -