http://arxiv.org/abs/1510.07043
We present a Bayesian approach to the redshift classification of emission-line galaxies when only a single emission line is detected spectroscopically. We consider the case of surveys for high-redshift ${\rm Ly{\alpha}}$-emitting galaxies (LAEs), which have traditionally been classified via an inferred rest-frame equivalent width $(W_{\rm Ly\alpha})$ greater than $20 {\rm \,\AA}$. Our Bayesian method relies on known prior probabilities in measured emission-line luminosity functions and equivalent width distributions for the galaxy populations in question, and it returns the probability that an object is an LAE given the characteristics observed. This approach will be directly relevant for the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX), which seeks to classify $\sim$$10^6$ emission-line galaxies into LAEs and low-redshift [O II] emitters. For a simulated HETDEX catalog with realistic measurement noise, our Bayesian method recovers $86\%$ of LAEs missed by the traditional $W_{\rm Ly\alpha} > 20 {\rm \,\AA}$ cutoff over $2 < z < 3$, outperforming the equivalent width (EW) cut in both contamination and incompleteness. Our method can trade off between contamination and incompleteness by adjusting the stringency of the probability requirement for classifying an observed object as an LAE in order to maximize the recovery of cosmological information. In our simulations of HETDEX, the Bayesian method reduces the uncertainty in cosmological distance measurements by $14\%$ with respect to the EW cut, equivalent to obtaining $29\%$ more data. This method enables us to use classification probabilities, rather than just object labels, in large-scale structure analyses, and can be applied to narrowband emission-line surveys as well as upcoming large spectroscopic surveys including Euclid and WFIRST.
A. Leung, V. Acquaviva, E. Gawiser, et. al.
Tue, 27 Oct 15
36/76
Comments: 15 pages, 7 figures, 5 tables, submitted to ApJ
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