Deep Learning Voigt Profiles. I. Single-Cloud Doublets

The Astronomical Journal, vol. 167, pp. 287

Abstract

Abstract Voigt profile (VP) decomposition of quasar absorption lines is key to studying intergalactic gas and the baryon cycle governing the formation and evolution of galaxies. The VP velocities, column densities, and Doppler b parameters inform us of the kinematic, chemical, and ionization conditions of these astrophysical environments. A drawback of traditional VP fitting is that it can be human-time intensive. With the coming next generation of large all-sky survey telescopes with multiobject high-resolution spectrographs, the time demands will significantly outstrip our resources. Deep learning pipelines hold the promise to keep pace and deliver science-digestible data products. We explore the application of deep learning convolutional neural networks (CNNs) for predicting VP-fitted parameters directly from the normalized pixel flux values in quasar absorption line profiles. A CNN was applied to 56 single-component Mg ii λ λ2796, 2803 doublet absorption line systems observed with HIRES and UVES (R = 45,000). The CNN predictions were statistically indistinct from those of a traditional VP fitter. The advantage is that, once trained, the CNN processes systems ∼105 times faster than a human expert fitting VP profiles by hand. Our pilot study shows that CNNs hold promise to perform bulk analysis of quasar absorption line systems in the future.

Authors 5

1. Bryson Stemock

   New Mexico State University

   Affiliation as printed

   Department of Astronomy, New Mexico State University, Las Cruces, NM 88003, USA

2. Christopher W. Churchill

   New Mexico State University

   Affiliation as printed

   Department of Astronomy, New Mexico State University, Las Cruces, NM 88003, USA

3. Avery Lee

   New Mexico State University

   Affiliation as printed

   Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA

4. Sultan Hassan

   New Mexico State University · Flatiron Institute · New York University · University of the Western Cape

   Affiliation as printed

   Center for Computational Astrophysics, Flatiron Institute, 162 5th Ave, New York, NY 10010, USA

   Center for Cosmology and Particle Physics, Department of Physics, New York University, 726 Broadway, New York, NY 10003, USA

   Department of Astronomy, New Mexico State University, Las Cruces, NM 88003, USA

   Department of Physics & Astronomy, University of the Western Cape, Cape Town 7535, South Africa

5. Caitlin C Doughty Aachen Leiden Observatory

   New Mexico State University · Leiden University · Leiden Observatory

   Affiliation as printed

   Department of Astronomy, New Mexico State University, Las Cruces, NM 88003, USA

   Leiden Observatory, Leiden University, PO Box 9513, 2300 RA, Leiden, The Netherlands