We describe the automated spectral classification, redshift determination, and parameter measurement pipeline in use for the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey III (SDSS-III) as of the survey's ninth data release (DR9), encompassing 831,000 moderate-resolution optical spectra. We give a review of the algorithms employed, and describe the changes to the pipeline that have been implemented for BOSS relative to previous SDSS-I/II versions, including new sets of stellar, galaxy, and quasar redshift templates. For the color-selected "CMASS" sample of massive galaxies at redshift 0.4 ≲ z ≲ 0.8 targeted by BOSS for the purposes of large-scale cosmological measurements, the pipeline achieves an automated classification success rate of 98.7% and confirms 95.4% of unique CMASS targets as galaxies (with the balance being mostly M stars). Based on visual inspections of a subset of BOSS galaxies, we find that approximately 0.2% of confidently reported CMASS sample classifications and redshifts are incorrect, and about 0.4% of all CMASS spectra are objects unclassified by the current algorithm which are potentially recoverable. The BOSS pipeline confirms that ∼51.5% of the quasar targets have quasar spectra, with the balance mainly consisting of stars and low signal-to-noise spectra. Statistical (as opposed to systematic) redshift errors propagated from photon noise are typically a few tens of kms -1 for both galaxies and quasars, with a significant tail to a few hundreds of kms -1 for quasars. We test the accuracy of these statistical redshift error estimates using repeat observations, finding them underestimated by a factor of 1.19-1.34 for galaxies and by a factor of two for quasars. We assess the impact of sky-subtraction quality, signal-to-noise ratio, and other factors on galaxy redshift success. Finally, we document known issues with the BOSS DR9 spectroscopic data set and describe directions of ongoing development.
All Science Journal Classification (ASJC) codes
- Astronomy and Astrophysics
- Space and Planetary Science
- methods: data analysis
- techniques: spectroscopic