On the Automated and Objective Detection of Emission Lines in Faint-Object Spectroscopy

Abstract

Modern spectroscopic surveys produce large spectroscopic databases, generally with sizes well beyond the scope of manual investigation. The need arises, therefore, for an automated line detection method with objective indicators for detection significance. In this paper, we present an automated and objective method for emission line detection in spectroscopic surveys and apply this technique to observed spectra from a Lyα emitter survey at z ∼ 2.7, obtained with the Hectospec spectrograph on the MMT Observatory (MMTO). The basic idea is to generate on-source (signal plus noise) and off-source (noise only) mock observations using Monte Carlo simulations, and calculate completeness and reliability values, (C,R), for each simulated signal. By comparing the detections from real data with the Monte Carlo results, we assign the completeness and reliability values to each real detection. From 1574 spectra, we obtain 881 raw detections and, by removing low reliability detections, we finalize 652 detections from an automated pipeline. Most of high completeness and reliability detections, (C,R) ≈ (1.0,1.0), are robust detections when visually inspected; the low C and R detections are also marginal on visual inspection. This method of detecting faint sources is dependent on the accuracy of the sky subtraction.