Host Galaxies of SDSS-II Supernovae
|University of Portsmouth|
Spectra of host galaxies of supernovae that were identified by the 2005-2008 SDSS supernova survey
An object whose
ANCILLARY_TARGET1 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||Number of Unique Primary Objects|
|SN_GAL1||36||Fiber assigned to the galaxy nearest the supernova position||4,169||3,055|
|SN_GAL2||37||Fiber assigned to second-nearest galaxy||111||91|
|SN_GAL3||38||Fiber assigned to third-nearest galaxy||25||22|
|SN_LOC||39||Fiber assigned to position of original supernova||422||353|
|SPEC_SN||40||Target selected from spectroscopy instead of photometric variation||2||2|
Between 2005 and 2008, the Sloan Digital Sky Survey Supernova Survey took repeated images of SDSS Stripe 82 (Frieman et al. 2008). These images were used to identify candidate type Ia supernovae, some of which were confirmed through spectroscopic follow-up on larger telescopes. These confirmed type Ia supernovae were then analyzed to obtain better constraints on cosmological parameters (Kessler et al. 2009, Sollerman et al. 2009, Lampeitl et al. 2010).
Although the supernova survey obtained real-time spectra for over 500 supernovae, the supernova survey detected many more transients, including many more supernovae, which we can detect through spectroscopy of the host galaxies of all these transient events.
Finding redshifts of host galaxies allows the light curves to be fit with fewer degrees of freedom, leading to identification of supernova type with higher confidence. These higher-confidence classifications will allow a much larger number of SNe Ia to be placed on the Hubble diagram (Campbell et al. 2013, Olmstead et al. 2014).
In this ancillary program, the host galaxies of candidate supernovae were drawn from a database containing 21,787 potentially variable objects determined from repeat imaging of Stripe 82. The next stage of target selection required signal detection in at least two passbands, after vetoing regions of bright stars, as well as variability from known active galactic nuclei (AGN) or variable stars. In total, the target list included 4,099 candidates, of different SN types and different confidence levels as determined from a Bayesian classification of light curve shapes (Sako et al. 2011).
Of these candidate targets, 3,743 were selected for this ancillary program. Fibers on the BOSS spectrograph were assigned to these host galaxies, with the goal of obtaining the host galaxy’s redshift, leading in turn to an improved supernova classification. Approximately one third of these 3,743 targets have lightcurves that do not resemble SNe; these are included as a control sample.
The redshifts and SNe classifications obtained by this ancillary target program will lead to a nearly complete sample of SNe Ia out to z < 0.4, and thus to an enhanced cosmological analysis. The new classifications will also enable a large statistical studies of the correlated properties between SNe Ia and their host galaxies (e.g. Kelly et al. 2010; Sullivan et al. 2010; Lampeitl et al. 2010; Brandt et al. 2010).
Targets by Sub-Program
Targets were assigned to sub-programs by visual inspection. Sub-program
SN_GAL1 was used for fibers positioned at the core of the host galaxy nearest the supernova. Sub-program
SN_GAL2 was used for fibers positioned at the core of the second-nearest host galaxy; similarly,
SN_GAL3 was used for fibers positioned at the core of the third-nearest host galaxy. Sub-program
SN_LOC was used for host galaxies whose spectra had already been measured by the SDSS; for these galaxies, the fibers were positioned at the location of the original variability of the supernova. Sub-program
SPEC_SN was used for host galaxies that had been targeted from SDSS spectra (e.g. Krughoff et al. 2011), rather than from photometric variability.
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Campbell, H., et al. 2013, ApJ, 763, 88C
Frieman, J. A., et al. 2008, AJ, 135, 338
Kelly, P. L., Hicken, M., Burke, D. L., Mandel, K. S., & Kirshner, R. P. 2010, ApJ, 715, 743
Kessler, R., et al. 2009, ApJS, 185, 32
Krughoff, K. S., Connolly, A. J., Frieman, J., SubbaRao, M., Kilper, G., & Schneider, D. P. 2011, ApJ, 731, 42
Lampeitl, H., et al. 2010, MNRAS, 401, 2331
Olmstead, M., et al. 2014, ApJ, 147, 75O
Sako, M., et al. 2011, ApJ, 738, 162
Sollerman, et al. 2009, AJ, 703, 1347
Sullivan, M., et al. 2010, MNRAS, 406, 782