@article{Photometrically:2744,
      recid = {2744},
      author = {Lasker, James Edwin},
      title = {Determination of the Volumetric Type Ia Supernova Rate  Using the Full 5-Year Dark Energy Survey Photometrically  Classified Sample},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2020-12},
      pages = {100},
      abstract = {In this thesis, I present a measurement of the volumetric  type Ia Supernova (SN Ia) rate from the full 5-year  supernova sample from the Dark Energy Survey (DES), the  first scientific analysis using this sample. After applying  selection criteria to the DES-SN light curves designed to  achieve a relatively pure and complete sample of  high-quality SN Ia events, the resulting sample comprises  2077 photometrically classified SNe Ia over the redshift  range 0.1 to 0.9, the largest homogeneously selected sample  in this redshift range. Based on simulations of the DES-SN  sample, adjusted so that the tails of the distribution of  the residuals in the supernova Hubble diagram match those  of the data, I estimate the contamination of the selected  sample from core-collapse SNe is 7.8% and correct the SN Ia  rate accordingly. This is the first rate analysis to use  only photometric supernova information, not relying on  spectroscopic SN typing, spectroscopic SN redshifts, or any  host information including spectroscopic and photometric  redshifts. This kind of analysis is necessary to utilize  the full statistical power of the DES-SN sample and will  become more critical with the arrival of the Vera Rubin  Observatory’s (VRO) Legacy Survey of Space and Time (LSST)  and its immense supernova sample (∼ 100, 000). In order to  demonstrate that this kind of analysis is viable, I  validate the photometric redshift fitting and photometric  classification code on simulations, using spectroscopic  information that is otherwise not used in the analysis. As  in previous SN Ia rate analyses (1; 2; 3), I find that the  volumetric SN Ia rate is consistent with (χ^2 = 13.95 , 6  DOF) a power-law form, R(z) = k(1 + z) β/yr/Mpc^3 over the  redshift range 0.1 to 0.9 and obtain k= 2.00 ± 0.21 (stat)  ± 0.24 (sys) x 10^{−5} and β = 1.82 ± 0.23 (stat) ± 0.31  (sys) using only DES data and a result of k = 2.03 ± 0.19  (stat) ± 0.18 (sys) x 10^{−5} and β = 1.79 ± 0.20 (stat) ±  0.27 (sys) when adding a prior from an SDSS rate analysis  to anchor the rate at low redshift. Both of these results  are consistent with measurements from prior rate analyses,  and they improve the statistical errors on parameters from  single-survey analyses while being the first to add a  systematic error analysis for their power law rate results.  The analysis was performed in a blinded fashion, obscuring  the exact value of the rate parameters until the analysis  was finalized to prevent such consistency from being forced  by analysis choices. The systematic errors in the rate  parameters are estimated by varying a number of selection  parameters that impact sample purity and completeness (in  both the simulations and the data sample), varying the  simulation core collapse model, and changing the redshift  range considered in the analysis. This data sample will be  used in a future analysis to measure a delay time  distribution between star formation and SN Ia explosion and  constrain SN Ia progenitor models.},
      url = {http://knowledge.uchicago.edu/record/2744},
      doi = {https://doi.org/10.6082/uchicago.2744},
}