Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Ly α Systems


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Abstract

jats:titleAbstract</jats:title> jats:pWe have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Lyjats:italicα</jats:italic> systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H jats:sci</jats:sc> column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixeljats:sup−1</jats:sup>. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N > 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms.</jats:p>

Description
Keywords
5101 Astronomical Sciences, 51 Physical Sciences
Journal Title
Astrophysical Journal, Supplement Series
Conference Name
Journal ISSN
0067-0049
1538-4365
Volume Title
259
Publisher
American Astronomical Society
Sponsorship
the Direc, Office of Science, Office of High Energy Physics of the U.S Department of Energy (DE-AC02-05CH11231)
U.S National Science Foundation, Division of Astronomical Sciences (AST-0950945)
National Key R&D Program of China (2018YFA0404503)
National Science Foundation of China (12073014)
Program Ranmon y Cajal of the Spanish Ministry of Science and Innovation (RYC-2018-025210)