BOSS-CLAM: Utilizing a Constrained Linear Absorption Model to Infer Stellar Parameters from BOSS Spectra
Large spectroscopic surveys require robust pipelines capable of inferring stellar parameters over a wide range of the Hertzsprung-Russell (HR) 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 ($T_\mathrm{eff}$), surface gravity ($\log g$), metallicity ($[\mathrm{Fe/H}]$), and $\alpha-$abundance ($[\alpha/\mathrm{M}]$) from continuum-normalized BOSS spectra. BOSS-CLAM maps stellar labels to Non-negative 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 $\sigma_{[\mathrm{Fe/H}]} \approx 0.15$ dex and $\sigma_{[\alpha/\mathrm{M}]} \approx 0.06$ dex at 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.