Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
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.
- Publication:
-
The Astrophysical Journal Supplement Series
- Pub Date:
- March 2022
- DOI:
- 10.3847/1538-4365/ac4504
- arXiv:
- arXiv:2201.00827
- Bibcode:
- 2022ApJS..259...28W
- Keywords:
-
- Quasar absorption line spectroscopy;
- Surveys;
- Astronomy data analysis;
- 1317;
- 1671;
- 1858;
- Astrophysics - Astrophysics of Galaxies;
- Astrophysics - Cosmology and Nongalactic Astrophysics
- E-Print:
- Accepted for publication in Astrophysical Journal Supplement 23 pages, 21 figures, 8 tables