eBOSS ELG Target Selection with Deep Photometry

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Photo of Johan Comparat
Johan Comparat
MPE, MPG, Munich, Germany
comparat@mpe.mpg.de

Summary

Photometry deeper than SDSS was used to assess algorithms for selection of Emission Line Galaxies (ELG) for spectroscopic observations

Finding Targets

An object whose ANCILLARY_TARGET2 value includes one or more of the bitmasks in the following table was targeted for spectroscopy as part of this ancillary target program. See SDSS bitmasks to learn how to use these values to identify objects in this ancillary target program.

Program (bit name) Bit number Target Description Number of Fibers
FAINT_ELG 18 Blue star-forming galaxy selected from CFHT-LS photometry 2,589

Description

This program used photometry extending to fainter limits than SDSS to select emission line galaxy (ELG) candidates, with the goal of assessing target selection algorithms for eBOSS. The sample was defined to help evaluate the completeness of the targeting sample and redshift success rates near the faint end of the ELG target population.

Data from this ancillary science program are described in Comparat et al. (2015), which measured the evolution of the bright end of the [OII] emission line luminosity function. Favole et al. (2016) use these data to derive halo occupation statistics of emission line galaxies.

Target Selection

Blue star-forming galaxies in the redshift range 0.6 < z < 1.2 were selected from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) Wide W3 field photometric redshift catalog T0007 (Ilbert et al. 2006; Coupon et al. 2009). Three plates (6931-6933) were observed centered on the same position, and targets were selected at a density of nearly 400 objects per square degree.

Selected objects satisfied the following constraints:

  • 20 < g < 22.8
  • -0.5 < (u – r) < 0.7(g – i) + 0.1

All photometry was based on CFHTLS MAG_AUTO magnitudes on the AB system. Objects with known redshift were excluded. A target was excluded if a redshift already existed from previous observations.

REFERENCES

Comparat, J., et al. 2015, A&A, 575, 40

Coupon, J., et al. 2009, A&A, 500, 981

Favole, G., et al., 2016, MNRAS, 461, 3421

Ilbert, O., et al. 2006, A&A, 457, 841

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