Construction of the damped Ly𝛼 absorber catalog for DESI DR2 Ly𝛼 BAO
We present the Damped Ly𝛼 Toolkit for automated detection and characterization of damped Ly𝛼 absorbers (DLAs) in quasar spectra. Our method uses quasar spectral templates with and without absorption from intervening DLAs to reconstruct observed quasar forest regions. The best-fitting model determines whether a DLA is present while estimating the redshift and HI column density. With an optimized quality cut on detection significance (Δ𝜒$^{2}_{𝑟}$ >0.03), the technique achieves an estimated 80% purity and 79% completeness when evaluated on simulated spectra with S/N>2 that are free of broad absorption lines (BALs). We provide a catalog containing candidate DLAs from the DLA Toolkit detected in DESI DR1 quasar spectra, of which 21 719 were found in S/N>2 spectra with predicted log 10 (𝑁 𝙷𝙸 )>20.3 and detection significance Δ𝜒$^{2}_{𝑟}$ >0.03. We compare the Damped Ly𝛼 Toolkit to two alternative DLA finders based on a convolutional neural network and Gaussian process models. We present a strategy for combining these three techniques to produce a high-fidelity DLA catalog from DESI DR2 for the Ly𝛼 forest baryon acoustic oscillation measurement. The combined catalog contains 41 152 candidate DLAs with log 10 (𝑁 𝙷𝙸 )>20.3 from quasar spectra with S/N>2. We estimate this sample to be approximately 85% pure and 79% complete when BAL quasars are excluded.