On the Automated and Objective Detection of Emission Lines in
  Faint-Object Spectroscopy
  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 1574 spectra, obtained with the Hectospec spectrograph on the MMT
Observatory (MMTO), to detect Lyman alpha emitters near z ~ 2.7. 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 649 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 at detecting faint sources is dependent on the accuracy
of the sky subtraction.
