  We have designed, developed, and applied a convolutional neural network (CNN)
architecture using multi-task learning to search for and characterize strong HI
Lya absorption in quasar spectra. Without any explicit modeling of the quasar
continuum nor application of the predicted line-profile for Lya from quantum
mechanics, our algorithm predicts the presence of strong HI absorption and
estimates the corresponding redshift zabs and HI column density NHI, with
emphasis on damped Lya systems (DLAs, absorbers with log NHI > 20.3). We tuned
the CNN model using a custom training set of DLAs injected into DLA-free quasar
spectra from the Sloan Digital Sky Survey (SDSS), data release 5 (DR5). Testing
on a held-back validation set demonstrates a high incidence of DLAs recovered
by the algorithm (97.4% as DLAs and 99% as an HI absorber with log NHI > 19.5)
and excellent estimates for zabs and NHI. Similar results are obtained against
a human-generated survey of the SDSS DR5 dataset. The algorithm yields a low
incidence of false positives and negatives but is challenged by overlapping
DLAs and/or very high NHI systems. We have applied this CNN model to the quasar
spectra of SDSS-DR7 and the Baryonic Oscillation Spectroscopic Survey (BOSS,
data release 12) and provide catalogs of 4,913 and 50,969 DLAs respectively
(including 1,659 and 9,230 high-confidence DLAs that were previously
unpublished). This work validates the application of deep learning techniques
to astronomical spectra for both classification and quantitative measurements.
