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Generalization from Low- to Moderate-resolution Spectra with Neural Networks for Stellar Parameter Estimation: A Case Study with DESI

  • Authors: Xiaosheng Zhao, Yuan-Sen Ting, Rosemary F.G. Wyse, Alexander S. Szalay, Yang Huang, László Dobos, Tamás Budavári, Viska Wei

Xiaosheng Zhao et al 2026 The Astrophysical Journal 1006 .

  • Provider: AAS Journals

Caption: Figure 2.

Comparison of [Fe/H] and [α/Fe] estimates between the DESI SP pipeline and MLP-based models, referenced against APOGEE DR17 labels. From left to right: DESI SP: DESI SP pipeline, MLP-Scratch: MLP trained from scratch on the same number of DESI spectra used for fine-tuning, MLP-LRS: MLP pretrained on LAMOST LRS (zero-shot application), and MLP-LRS (fine-tuned): MLP pretrained on LAMOST LRS and fine-tuned with DESI spectra. The first row shows [Fe/H] results; the second row shows [α/Fe]. Legends list the number of test stars (N), coefficient of determination (R2), and the robustly estimated standard deviation of the residuals (σ, computed after 3σ clipping with sigma_clip in astropy). All sources are restricted to Teff > 4000 K. The dashed black line indicates the ideal one-to-one relation, and the gray dotted lines mark 0.2 dex deviations. The fine-tuning set consists of 2069 stars. A comparison using the clean, calibrated DESI SP subset is provided in Appendix D.

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