A Low-Dimensional Chemical Representation of High-Resolution Stellar Disk Spectra: Residual Abundance Signatures
Abstract
Stellar abundances are generated by complex chemical enrichment pathways yet show strong correlation structure. We test how well high-quality spectra of low-alpha disk stars can be reconstructed using low-dimensional models. By validating model performance in flux space, we quantify and locate where residual information persists. Using HARPS spectra (R = 115,000, SNR > 100) and labels derived with Korg, we train generative models (The Cannon) conditioned on stellar parameters Teff, log(g), [M/H], vsini, and vmic, and three abundance labels, [Ti/H], [Y/H], and [Nd/H], representing distinct nucleosynthetic families. Evaluating model performance on unseen test stars through flux residuals, the model reproduces the global spectra to a median residual amplitude of 0.3%. Deviations in 15 absorption features for unlabelled elements are typically < 0.7%. Residuals of ~1% correspond to abundance differences of 0.01-0.05 dex. Degrading the spectra to medium resolution (R = 28,000) decreases the median residual amplitude from 0.3% to 0.2%, redistributing the residual signal over the instrumental line profile. Our publicly available model reproduces features from 29 elements to sub-percent precision across -1.13 < [M/H] < 0.29. This demonstrates that the dominant chemical variation encoded in low-alpha disk spectra can be captured by a low-dimensional model, while weaker chemical information remains encoded in flux residuals. High-resolution, high-SNR spectra (R ~ 100,000, SNR > 100) are essential to resolve the hierarchical layers of chemical information beyond this low-dimensional structure. The model provides a benchmark for assessing the chemical information accessible to current surveys like 4MOST and GALAH 2, as well as upcoming facilities like VLT/HRMOS and GMT/G-CLEF.
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BibTeX
@misc{aquilina2026lowdimensional,
title = {{A Low-Dimensional Chemical Representation of High-Resolution Stellar Disk Spectra: Residual Abundance Signatures}},
author = {Sarah E. Aquilina and Melissa K. Ness and Sven Buder},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.39118},
eprint = {2610.39118},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.39118},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Low-Dimensional Chemical Representation of High-Resolution Stellar Disk Spectra: Residual Abundance Signatures
AU - Sarah E. Aquilina
AU - Melissa K. Ness
AU - Sven Buder
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.39118
UR - https://arxiv.org/abs/2610.39118
AB - Stellar abundances are generated by complex chemical enrichment pathways yet show strong correlation structure. We test how well high-quality spectra of low-alpha disk stars can be reconstructed using low-dimensional models. By validating model performance in flux space, we quantify and locate where residual information persists. Using HARPS spectra (R = 115,000, SNR > 100) and labels derived with Korg, we train generative models (The Cannon) conditioned on stellar parameters Teff, log(g), [M/H], vsini, and vmic, and three abundance labels, [Ti/H], [Y/H], and [Nd/H], representing distinct nucleosynthetic families. Evaluating model performance on unseen test stars through flux residuals, the model reproduces the global spectra to a median residual amplitude of 0.3%. Deviations in 15 absorption features for unlabelled elements are typically < 0.7%. Residuals of ~1% correspond to abundance differences of 0.01-0.05 dex. Degrading the spectra to medium resolution (R = 28,000) decreases the median residual amplitude from 0.3% to 0.2%, redistributing the residual signal over the instrumental line profile. Our publicly available model reproduces features from 29 elements to sub-percent precision across -1.13 < [M/H] < 0.29. This demonstrates that the dominant chemical variation encoded in low-alpha disk spectra can be captured by a low-dimensional model, while weaker chemical information remains encoded in flux residuals. High-resolution, high-SNR spectra (R ~ 100,000, SNR > 100) are essential to resolve the hierarchical layers of chemical information beyond this low-dimensional structure. The model provides a benchmark for assessing the chemical information accessible to current surveys like 4MOST and GALAH 2, as well as upcoming facilities like VLT/HRMOS and GMT/G-CLEF.
ER -