PISP: Projected-Space Inference of Stellar Parameters

Abstract To improve the accuracy and efficiency of high-dimensional stellar parameter inference in large spectroscopic datasets, we propose a projection-assisted parameter-inference framework—Projected-Space Inference of Stellar Parameters (PISP). PISP constructs an orthonormal basis and optimizes in the projected space, reducing the impact of parameter correlations on inference. The basis is constructed using either principal component analysis (PCA) or the active-subspace (AS) method and is combined with two inference strategies—Non-L1, which optimizes the projection coefficients for a user-specified projected dimensionality, and L1, which introduces L1 regularization in the full projected space to adaptively select projection directions—yielding four strategies: PCA-Non-L1, AS-Non-L1, PCA-L1, and AS-L1. For different computational scenarios, we implement two versions: PISP-CurveFit for fast single-spectrum inference and PISP-Adam for large-scale GPU-parallel inference. Using a fully connected neural network and a residual network as spectral emulators, we evaluate PISP on Kurucz synthetic spectra and on 722,896 APOGEE DR17 observed spectra. Compared to the baseline strategy, PISP improves inference accuracy for multiple parameters across all emulator–optimizer combinations. In synthetic data, PCA-L1 performs best, reducing the standard deviation of differences ( σ (Δ)) by at least 0.01 dex for 12 of 20 elemental abundances, with [N/H], [O/H], [Na/H], [Co/H], [P/H], [V/H], [Cu/H] showing 0.05–0.72 dex reductions. In observed data, PCA-Non-L1 reduces σ (Δ) by >30 K for effective temperature and by at least 0.01 dex for 9 of 17 elemental abundances, with [O/H], [Na/H], [V/H] showing 0.05–0.20 dex reductions, while achieving a ∼4× efficiency gain, slightly outperforming PCA-L1.

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Publication Details

Journal
The Astrophysical Journal Supplement Series
Published
2026-04-16
DOI
https://doi.org/10.3847/1538-4365/ae4ed2
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
0.00

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article

PISP: Projected-Space Inference of Stellar Parameters

Ke-Fei Wu, H. R. A. Jones, Shuguo Ma, Xiao Kong et al.
The Astrophysical Journal Supplement Series
Stellar, planetary, and galactic studies
article

PISP: Projected-Space Inference of Stellar Parameters

Ke-Fei Wu, H. R. A. Jones, Shuguo Ma, Xiao Kong, Yin-Bi Li, Shuo Li, Li-Li Wang, Hao Zeng, Xiao-Xiao Ma, A-Li Luo, Zhi-Hua Zhong, Ming-Hui Jia, Hai-Ling Lu, Jun-Chao Liang
article en

Abstract

Abstract To improve the accuracy and efficiency of high-dimensional stellar parameter inference in large spectroscopic datasets, we propose a projection-assisted parameter-inference framework—Projected-Space Inference of Stellar Parameters (PISP). PISP constructs an orthonormal basis and optimizes in the projected space, reducing the impact of parameter correlations on inference. The basis is constructed using either principal component analysis (PCA) or the active-subspace (AS) method and is combined with two inference strategies—Non-L1, which optimizes the projection coefficients for a user-specified projected dimensionality, and L1, which introduces L1 regularization in the full projected space to adaptively select projection directions—yielding four strategies: PCA-Non-L1, AS-Non-L1, PCA-L1, and AS-L1. For different computational scenarios, we implement two versions: PISP-CurveFit for fast single-spectrum inference and PISP-Adam for large-scale GPU-parallel inference. Using a fully connected neural network and a residual network as spectral emulators, we evaluate PISP on Kurucz synthetic spectra and on 722,896 APOGEE DR17 observed spectra. Compared to the baseline strategy, PISP improves inference accuracy for multiple parameters across all emulator–optimizer combinations. In synthetic data, PCA-L1 performs best, reducing the standard deviation of differences ( σ (Δ)) by at least 0.01 dex for 12 of 20 elemental abundances, with [N/H], [O/H], [Na/H], [Co/H], [P/H], [V/H], [Cu/H] showing 0.05–0.72 dex reductions. In observed data, PCA-Non-L1 reduces σ (Δ) by >30 K for effective temperature and by at least 0.01 dex for 9 of 17 elemental abundances, with [O/H], [Na/H], [V/H] showing 0.05–0.20 dex reductions, while achieving a ∼4× efficiency gain, slightly outperforming PCA-L1.

The Astrophysical Journal Supplement SeriesVol. 284(1)
Nanjing University of Chinese Medicine (CN), University of Hertfordshire (GB), Chinese Academy of Sciences (CN), Institute of Automation (CN), National Astronomical Observatories (CN), University of Chinese Academy of Sciences (CN), Dezhou University (CN), The University of Tokyo (JP)
Smithsonian Institution, U.S. Department of Energy, Alfred P. Sloan Foundation, Yale University, Carnegie Institution of Washington, Vanderbilt University, Ohio State University, University of Utah, University of Washington, Johns Hopkins University, Carnegie Mellon University, University of Notre Dame, University of Portsmouth, New Mexico State University, York University, University of Oxford, National Natural Science Foundation of China, Chinese Academy of Sciences, Ministério da Ciência, Tecnologia e Inovação, Universidad Nacional Autónoma de México, Max-Planck-Institut für Astrophysik, Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Canarias, Office of Science, University of Colorado Boulder, Leibniz-Gemeinschaft, National Key Research and Development Program of China, Lawrence Berkeley National Laboratory
Openalex Percentile: Top 19%
Stellar, planetary, and galactic studies
DOIOpen Access PDFOpenAlex
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