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Peer-reviewedCC BY 4.0The Astrophysical Journal Supplement Series

BOSS-CLAM: Utilizing a Constrained Linear Absorption Model to Infer Stellar Parameters from BOSS Spectra

  • Ilija Medan1,2
  • Andrew R. Casey3,4
  • Alexander P. Ji5,6
  • Jonah M. Otto7
  • Kayvon Sharifi8
  • Zachary Way8
  • Madeleine McKenzie9
  • Natalie R. Myers10
  • Keivan G. Stassun1
  • Peter J. Smith11,12
  • Andrew Tkachenko13
  • Vedant Chandra14
4 more authors
  • Michael R. Blanton15,16
  • Peter M. Frinchaboy7
  • Guy S. Stringfellow17
  • Sean Morrison18
18 affiliations
  1. 1Vanderbilt University
  2. 2University of Toronto
  3. 3Monash University
  4. 4Flatiron Institute
  5. 5University of Chicago
  6. 6NSF-Simons AI Institute for the Sky (SkAI)
  7. 7Texas Christian University
  8. 8Georgia State University
  9. 9Observatories of the Carnegie Institution for Science
  10. 10Johns Hopkins University
  11. 11Max Planck Institute for Astronomy
  12. 12University of Heidelberg
  13. 13Institute of Astronomy
  14. 14Center for Astrophysics ∣Harvard & Smithsonian
  15. 15The Observatories of the Carnegie Institution for Science
  16. 16New York University
  17. 17University of Colorado Boulder
  18. 18University of Illinois at Urbana-Champaign
Open at sourcePDFDOICite

Abstract

Large spectroscopic surveys require robust pipelines capable of inferring stellar parameters over a wide range of the Hertzsprung–Russell (H-R) diagram from data of varying quality. SDSS-V is one such survey, where the data from the lower-resolution, optical BOSS spectrograph will provide a large dataset covering a wide range of Galactic stellar populations. To better analyze these data, we present BOSS-CLAM, a generative, forward-modeling pipeline for inferring effective temperature ( T eff ), surface gravity ( log g ), metallicity ([Fe/H]), and α -abundance ([ α /M]) from continuum-normalized BOSS spectra. BOSS-CLAM maps stellar labels to nonnegative matrix factorization (NMF) basis vector weights via a polynomial mapping jointly optimized with the spectral decomposition, which provides a more flexible framework for working with the lower-resolution BOSS data. Additionally, training labels are drawn from four complementary sources (ASPCAP, BOSS-MINESweeper , wide binaries, and a hot star validation sample), which enables coverage from cool M dwarfs through hot OB stars, and across a wide range of metallicity. We infer parameters for 1,708,214 BOSS spectra, with a recommended clean catalog of 915,514 sources. Validation against open and globular clusters demonstrates homogeneous, accurate abundances across a wide range of metallicity. Wide binary tests yield abundance uncertainties of σ [Fe/H] ≈ 0.15 dex and σ [ α /M] ≈ 0.06 dex at signal-to-noise ratio (SNR) = 10. Finally, we demonstrate that the BOSS-CLAM catalog recovers known chemical structure of the Milky Way disk and is well suited for Galactic archaeology, chemical tagging, and stellar population modeling. The pipeline, trained model, and catalog are publicly released as part of SDSS-V DR20.

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Cite

BibTeXRIS
BibTeX
@article{medan2026bossclam,
  title = {{BOSS-CLAM: Utilizing a Constrained Linear Absorption Model to Infer Stellar Parameters from BOSS Spectra}},
  author = {Ilija Medan and Andrew R. Casey and Alexander P. Ji and Jonah M. Otto and Kayvon Sharifi and Zachary Way and Madeleine McKenzie and Natalie R. Myers and Keivan G. Stassun and Peter J. Smith and Andrew Tkachenko and Vedant Chandra and Michael R. Blanton and Peter M. Frinchaboy and Guy S. Stringfellow and Sean Morrison},
  journal = {The Astrophysical Journal Supplement Series},
  year = {2026},
  month = oct,
  doi = {10.3847/1538-4365/aea56f},
  url = {https://iopscience.iop.org/article/10.3847/1538-4365/aea56f},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - BOSS-CLAM: Utilizing a Constrained Linear Absorption Model to Infer Stellar Parameters from BOSS Spectra
AU  - Ilija Medan
AU  - Andrew R. Casey
AU  - Alexander P. Ji
AU  - Jonah M. Otto
AU  - Kayvon Sharifi
AU  - Zachary Way
AU  - Madeleine McKenzie
AU  - Natalie R. Myers
AU  - Keivan G. Stassun
AU  - Peter J. Smith
AU  - Andrew Tkachenko
AU  - Vedant Chandra
AU  - Michael R. Blanton
AU  - Peter M. Frinchaboy
AU  - Guy S. Stringfellow
AU  - Sean Morrison
PY  - 2026
DA  - 2026/10/13
JO  - The Astrophysical Journal Supplement Series
DO  - 10.3847/1538-4365/aea56f
UR  - https://iopscience.iop.org/article/10.3847/1538-4365/aea56f
AB  - Large spectroscopic surveys require robust pipelines capable of inferring stellar parameters over a wide range of the Hertzsprung–Russell (H-R) diagram from data of varying quality. SDSS-V is one such survey, where the data from the lower-resolution, optical BOSS spectrograph will provide a large dataset covering a wide range of Galactic stellar populations. To better analyze these data, we present BOSS-CLAM, a generative, forward-modeling pipeline for inferring effective temperature ( T eff ), surface gravity ( log g ), metallicity ([Fe/H]), and α -abundance ([ α /M]) from continuum-normalized BOSS spectra. BOSS-CLAM maps stellar labels to nonnegative matrix factorization (NMF) basis vector weights via a polynomial mapping jointly optimized with the spectral decomposition, which provides a more flexible framework for working with the lower-resolution BOSS data. Additionally, training labels are drawn from four complementary sources (ASPCAP, BOSS-MINESweeper , wide binaries, and a hot star validation sample), which enables coverage from cool M dwarfs through hot OB stars, and across a wide range of metallicity. We infer parameters for 1,708,214 BOSS spectra, with a recommended clean catalog of 915,514 sources. Validation against open and globular clusters demonstrates homogeneous, accurate abundances across a wide range of metallicity. Wide binary tests yield abundance uncertainties of σ [Fe/H] ≈ 0.15 dex and σ [ α /M] ≈ 0.06 dex at signal-to-noise ratio (SNR) = 10. Finally, we demonstrate that the BOSS-CLAM catalog recovers known chemical structure of the Milky Way disk and is well suited for Galactic archaeology, chemical tagging, and stellar population modeling. The pipeline, trained model, and catalog are publicly released as part of SDSS-V DR20.
SN  - 0067-0049
SN  - 1538-4365
ER  -