Detection of metal absorption lines in quasar spectra: a neural network approach using U-Net

Current and future large spectroscopic surveys are significantly enhancing the volume and resolution of quasar spectra that are observed, which requires the creation of efficient and precise automated techniques to detect absorption features. This study focuses on the detection of metal absorption features using a novel U-Net model on WEAVE-like mock spectra in the quasar rest-frame wavelength interval 1230, at most λ RF at most 3095,. We test the network performance for absorption detection both on ideal data and after simulating the continuum fitting step as applied on real data. The performance of these architectures is evaluated by the completeness, purity, and F1 score reached in bins of signal-to-noise (S/ N line) for the absorption lines and with the absolute fractional flux error for the continuum. The ability to recover the correct line centers is also studied. The U-Net reaches scores of about 90% for all metrics (completeness, purity, and F1 score) at S/ N line about 4. All false positive detections with S/ N line at least 5 fall in the tails of the broad Ly α absorbers distribution of damped Ly α systems. The combination of continuum fitting and line detections has negligible effects on the detection performance at S/ N line at least 4. Our proposed U-Net architecture offers a competitive tool for the analysis of absorption lines in current and upcoming large spectroscopic surveys and is well-suited to the identification of any absorption line feature.