The DEEP2 Galaxy Redshift Survey: Redshift Identification of Single-Line Emission Galaxies
arXiv:astro-ph/0701747 · doi:10.1086/513464
Abstract
We present two methods for determining spectroscopic redshifts of galaxies in the DEEP2 survey which display only one identifiable feature, an emission line, in the observed spectrum ("single-line galaxies"). First, we assume each single line is one of the four brightest lines accessible to DEEP2: Halpha, [OIII] 5007, Hbeta, or [OII] 3727. Then, we supplement spectral information with BRI photometry. The first method, parameter space proximity (PSP), calculates the distance of a single-line galaxy to galaxies of known redshift in (B-R), (R-I), R, observed wavelength parameter space. The second method is an artificial neural network (ANN). Prior information, such as allowable line widths and ratios, rules out one or more of the four lines for some galaxies in both methods. Based on analyses of evaluation sets, both methods are nearly perfect at identifying blended [OII] doublets. Of the lines identified as Halpha in the PSP and ANN methods, 91.4% and 94.2% respectively are accurate. Although the methods are not this accurate at discriminating between [OIII] and Hbeta, they can identify a single line as one of the two, and the ANN method in particular unambiguously identifies many [OIII] lines. From a sample of 640 single-line spectra, the methods determine the identities of 401 (62.7%) and 472 (73.8%) single lines, respectively, at accuracies similar to those found in the evaluation sets.
11 pages, 6 figures, accepted to ApJ
References in corpus (12)
- Science Objectives and Early Results of the DEEP2 Redshift Survey
- ANNz: estimating photometric redshifts using artificial neural networks
- Galaxies in the Hubble Ultra Deep Field: I. Detection, Multiband Photometry, Photometric Redshifts, and Morphology
- Estimating photometric redshifts with artificial neural networks
- Evolution and Color-Dependence of the Galaxy Angular Correlation Function: 350,000 Galaxies in 5 Square Degrees
- Measuring Galaxy Environments with Deep Redshift Surveys
- Photometric redshifts with the Multilayer Perceptron Neural Network: application to the HDF-S and SDSS
- Novel Methods for Predicting Photometric Redshifts from Broad Band Photometry using Virtual Sensors
- THE CANADA-FRANCE REDSHIFT SURVEY III: "Single Emission Line" Objects, Analysis of Repeat Observations, and Spectroscopic Identifications in the 1415+52 and 2215+00 Fields
- A Census of Object Types and Redshift Estimates in the SDSS Photometric Catalog from a Trained Decision-Tree Classifier
- Caltech Faint Galaxy Redshift Survey VII: Data Analysis Techniques and Redshifts in the Field J0053+1234
- The Canada-France Deep Fields III: Photometric Redshift Distribution to I(AB) ~ 24
Cited by in corpus (12)
- The DEEP2 Galaxy Redshift Survey: Design, Observations, Data Reduction, and Redshifts
- The Great Observatories Origins Deep Survey. VLT/VIMOS Spectroscopy in the GOODS-South Field
- The Arizona CDFS Environment Survey (ACES): A Magellan/IMACS Spectroscopic Survey of the Chandra Deep Field South
- The WiggleZ Dark Energy Survey: Final Data Release and the Metallicity of UV-Luminous Galaxies
- Serendipitous Discovery of an Overdensity of Lyman-Alpha Emitters at z~4.8 in the Cl1604 Supercluster Field
- Emission-Line Galaxies from the PEARS Hubble Ultra Deep Field: A 2-D Detection Method and First Results
- Interloper bias in future large-scale structure surveys
- RELICS: Strong Lensing analysis of the galaxy clusters Abell S295, Abell 697, MACS J0025.4-1222, and MACS J0159.8-0849
- The DEEP2 Redshift Survey: Lyman Alpha Emitters in the Spectroscopic Database
- Measuring galaxy [OII] emission line doublet with future ground-based wide-field spectroscopic surveys
- High-redshift cosmology with oxygen lines from H surveys
- Precise self-calibration of interloper bias in spectroscopic surveys