Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data
Abstract
Abstract We present an automatic method based on machine learning convolutional neural network (CNN) architecture to detect Ly α emitters (LAEs) hidden in the Data Release 1 spectroscopic data set of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at z > 2. To uncover those sources, we first visually inspect 4134 DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly α redshifts. The final model yields 95.2% purity and 95.9% completeness for detecting LAEs. We apply this model to approximately 2 × 10 6 spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from z ∼ 2 to 3.5 within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly α luminosity L Ly α ≳ 10 43 erg s −1 . The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and Mg ii emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.
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Authors: Jui-Kuan Chan, Ting-Wen Lan, J Xavier Prochaska, Shun Saito, Aguilar Jl., Steven P. Ahlen, D Bianchi, D. Brooks, A. Cuceu, A. de la Macorra, Biprateep Dey, P. Doel, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, Satya Gontcho A Gontcho, G. Gutierrez, C. Hahn, J. Jimenez, R. Joyce, Stéphanie Juneau, D. Kirkby, A. Kremin, Martin Landriau, Marc Manera, Aaron Meisner, R. Miquel, J. Moustakas, S. Nadathur, Will J. Percival, Claire Poppett, F. Prada, Ignasi Pérez-Ràfols, G. Rossi, E. J. Sanchez, D. Schlegel, Michael S. Schubnell, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, H. Zou
Institutions: University of Pittsburgh, Universidad Nacional Autónoma de México, Chinese Academy of Sciences, University of Virginia, University of Michigan, University of Toronto, Boston University, Consejo Superior de Investigaciones Científicas, University College London, The University of Tokyo, Universitat Autònoma de Barcelona, National Taiwan University, The University of Texas at Austin, University of Waterloo, Institució Catalana de Recerca i Estudis Avançats, Missouri University of Science and Technology, Universitat Politècnica de Catalunya, University of California, Santa Cruz, University of Milan, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, University of Portsmouth, University of California System, Sejong University, Lawrence Berkeley National Laboratory, Fermi National Accelerator Laboratory, University of Siena, Institut d'Estudis Espacials de Catalunya, NSF NOIRLab, Instituto de Astrofísica de Andalucía, Institute of Astronomy and Astrophysics, Academia Sinica, National Institute for Astrophysics, Universidad de Los Andes, Brera Astronomical Observatory, Institute for High Energy Physics, Perimeter Institute, University of California Observatories