English

The Spectroscopic Data Processing Pipeline for the Dark Energy Spectroscopic Instrument

Instrumentation and Methods for Astrophysics 2023-03-15 v2 Cosmology and Nongalactic Astrophysics

Abstract

We describe the spectroscopic data processing pipeline of the Dark Energy Spectroscopic Instrument (DESI), which is conducting a redshift survey of about 40 million galaxies and quasars using a purpose-built instrument on the 4-m Mayall Telescope at Kitt Peak National Observatory. The main goal of DESI is to measure with unprecedented precision the expansion history of the Universe with the Baryon Acoustic Oscillation technique and the growth rate of structure with Redshift Space Distortions. Ten spectrographs with three cameras each disperse the light from 5000 fibers onto 30 CCDs, covering the near UV to near infrared (3600 to 9800 Angstrom) with a spectral resolution ranging from 2000 to 5000. The DESI data pipeline generates wavelength- and flux-calibrated spectra of all the targets, along with spectroscopic classifications and redshift measurements. Fully processed data from each night are typically available to the DESI collaboration the following morning. We give details about the pipeline's algorithms, and provide performance results on the stability of the optics, the quality of the sky background subtraction, and the precision and accuracy of the instrumental calibration. This pipeline has been used to process the DESI Survey Validation data set, and has exceeded the project's requirements for redshift performance, with high efficiency and a purity greater than 99 percent for all target classes.

Keywords

Cite

@article{arxiv.2209.14482,
  title  = {The Spectroscopic Data Processing Pipeline for the Dark Energy Spectroscopic Instrument},
  author = {J. Guy and S. Bailey and A. Kremin and Shadab Alam and D. M. Alexander and C. Allende Prieto and S. BenZvi and A. S. Bolton and D. Brooks and E. Chaussidon and A. P. Cooper and K. Dawson and A. de la Macorra and A. Dey and Biprateep Dey and G. Dhungana and D. J. Eisenstein and A. Font-Ribera and J. E. Forero-Romero and E. Gaztañaga and S. Gontcho A Gontcho and D. Green and K. Honscheid and M. Ishak and R. Kehoe and D. Kirkby and T. Kisner and Sergey E. Koposov and Ting-Wen Lan and M. Landriau and L. Le Guillou and Michael E. Levi and C. Magneville and Christopher J. Manser and P. Martini and Aaron M. Meisner and R. Miquel and J. Moustakas and Adam D. Myers and Jeffrey A. Newman and Jundan Nie and N. Palanque-Delabrouille and W. J. Percival and C. Poppett and F. Prada and A. Raichoor and C. Ravoux and A. J. Ross and E. F. Schlafly and D. Schlegel and M. Schubnell and Ray M. Sharples and Gregory Tarlé and B. A. Weaver and Christophe Yèche and Rongpu Zhou and Zhimin Zhou and H. Zou},
  journal= {arXiv preprint arXiv:2209.14482},
  year   = {2023}
}

Comments

AJ, revised version, 55 pages, 55 figures, 4 tables