Damped Lyman-alpha (DLA) Quasar Catalog

This document describes the content and construction of DLA catalogs for DESI Early Data Release (EDR) Fuji Production (release date May 2023).

Overview

Our VAC file contains three DLA catalogs for Survey Validation survey (SV1, SV2, and SV3).

• fuji-sv1-dark-combine-dlacatalog.fits
• fuji-sv3-dark-combine-dlacatalog.fits
• fuji-sv2-dark-combine-dlacatalog.fits

These catalogs combine damped Lyman-alpha (DLA) quasar catalogs from two machine learning DESI DLA finders: a convolutional neural network (CNN) model and a Gaussian Process (GP) model. For more information about these DESI DLA finders see Wang et al. (2022).

Data Access

Data URL: https://data.desi.lbl.gov/public/edr/vac/edr/dla/

NERSC access for DESI collaborators:

/global/cfs/cdirs/desi/public/edr/vac/edr/dla/

Documentation

Here is the strategy to combine catalogs:

• Match each absorber from two models (dv < 800 km/s).
• For absorbers detected by both models: use GP_NHI and GP_Z_DLA from GP.
• For absorbers only detected by the CNN model: use CNN_NHI and CNN_Z_DLA from CNN.
• For absorbers only detected by the GP model: use GP_NHI and GP_Z_DLA from GP.

Column Name	Description
TARGET_RA	Target Right Ascension (J2000 decimal degrees)
TARGET_DEC	Target Declination (J2000 decimal degrees)
Z_QSO	Best-fit redshift after masking
TARGETID	Unique 64-bit identifier for each object observed by DESI
S2N	Uses slices in 1420–1480Å in QSO rest-frame to calculate the continuum signal-to-noise.
DLAID	Unique DLA ID, TARGETID+ 3-bit code. The 3-bit code is sorted by redshift from low to high, ‘000’ to ‘001’, etc.
CNN_NHI	CNN model HI column density (cm-2). If this DLA was not detected by one DLA finder, then this finder’s prediction will set to 0.
GP_NHI	GP model HI column density (cm-2). If this DLA was not detected by one DLA finder, then this finder’s prediction will set to 0.
CNN_Z_DLA	CNN model redshift. If this DLA was not detected by one DLA finder, then this finder’s prediction will set to 0.
GP_Z_DLA	GP model redshift. If this DLA was not detected by one DLA finder, then this finder’s prediction will set to 0.
CNN_DLA_CONFIDENCE	The confidence level predicted by CNN DLA finder. We suggest to select absorbers with ‘CNN_DLA_CONFIDENCE’>0.2 as valid detections for ‘S2N’>3, ‘DLA_CONFIDENCE’>0.3 for ‘S2N’<3.
GP_DLA_CONFIDENCE	The confidence level predicted by GP DLA finder. We suggest to select absorbers with ‘GP_DLA_CONFIDENCE’>0.9 as valid detections. See descriptions in Ho et al. (2021).
ABSORBER_TYPE	DLA (NHI > 20.3), SUBDLA (NHI < 20.3), LYB (Lyman-beta absorbers corresponding to DLAs in the same sightline)
NHI	For absorbers detected by both models use NHI=GP_NHI. For absorbers only detected by the CNN model use NHI=CNN_NHI. For absorbers only detected by the GP model use NHI=GP_NHI.
Z_DLA	For absorbers detected by both models use Z_DLA=GP_Z_DLA. For absorbers only detected by the CNN model use Z_DLA=CNN_Z_DLA. For absorbers only detected by the GP model use Z_DLA=GP_Z_DLA.

Contact

These catalogs were generated by DLA Finder Group, contact Jiaqi Zou if you have any questions.