Damped Lya Catalog from the DLA Toolkit¶
Overview¶
This catalog contains candidate DLAs detected by the Damped Lya Toolkit in DESI Y1 quasar spectra with 2.0 < Z_QSO < 4.25. Detailed information about the catalog and the DLA Toolkit performance is provided in Brodzeller et al. (2025). We recommend quality cuts of SNR_REDSIDE > 2, NHI > 20.3, DELTACHI2 > 0.3, and excluding BAL quasars using the zlya VAC for best performance. With these cuts, v1.0 is estimated to be 80% pure and 79% complete.
Data Access¶
Data URL: https://data.desi.lbl.gov/public/dr1/vac/dr1/dla-toolkit
NERSC access:
/global/cfs/cdirs/desi/public/dr1/vac/dr1/dla-toolkit
Documentation¶
Files¶
dlacat-dlatoolkit-dr1-main-dark-v2.0.fits- DLA catalog
Data Model¶
| Name | Type | Units | Description |
|---|---|---|---|
| TARGETID | int64 | - | Unique DESI target ID |
| RA | float64 | deg | right ascension in decimal degrees (J2000) |
| DEC | float64 | deg | declination in decimal degrees (J2000) |
| Z_QSO | float64 | - | quasar redshift |
| SNR_FOREST | float64 | - | mean pixel S/N between 1040 and 1205 A in Z_QSO rest frame |
| SNR_REDSIDE | float64 | - | mean pixel S/N between 1420 and 1480 A in Z_QSO rest frame |
| DLAID | string | - | unique identifier for DLAs |
| Z_DLA | float64 | - | DLA redshift |
| Z_DLA_ERR | float64 | - | error on Z_DLA estimated with parabola fit to chi2 minimum |
| NHI | float64 | - | log10 HI column density of DLA |
| NHI_ERR | float64 | - | error on log10 HI column density estimated with parabola fit to chi2 minimum |
| COEFF | float64[4] | - | Coefficients on quasar PCA vectors for DLA solution |
| DELTACHI2 | float64 | - | improvement in reduced chi2 when DLA is included in fit |
Change Log¶
v2.0¶
Version 2.0 was produced with an updated version (tag v1.0.0) of the DLA Toolkit. he changes to the DLA Toolkit address the low completeness reported at high S/N (Fig. 6 of Brodzeller et al. 2025). The primary updates adjust the NHI chi2 surface to avoid local minima traps. With the same quality cuts recommended above, v2.0 is an estimated 82% pure and 84% complete.Predicted redshift accuracy is the same as reported in Brodzeller et al. (2025). Predicted NHI accuracy improved to an average \Delta(NHI) = 0.0055 (std = 0.248). The gains are dominated by classifications at S/N>10.
v1.0¶
Original release.
Contact¶
Contact Allyson Brodzeller for questions about this catalog