Deep Learning Voigt Profiles. I. Single-Cloud Doublets

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.