Wang, Ben;
Zou, Jiaqi;
Cai, Zheng;
Prochaska, J Xavier;
Sun, Zechang;
Ding, Jiani;
Font-Ribera, Andreu;
... Zhou, Zhimin; + view all
(2022)
Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Ly alpha Systems.
Astrophysical Journal Supplement Series
, 259
(1)
, Article 28. 10.3847/1538-4365/ac4504.
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Abstract
We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Lyα 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 i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel-1. 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.
| Type: | Article |
|---|---|
| Title: | Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Ly alpha Systems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3847/1538-4365/ac4504 |
| Publisher version: | https://doi.org/10.3847/1538-4365/ac4504 |
| Language: | English |
| Additional information: | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, EVOLUTION, FOREST, ABSORPTION, ABSORBERS, QUASARS, MODEL |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10146075 |
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