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We present the full Hubble diagram of photometrically-classified Type Iasupernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN).DES-SN discovered more than 20,000 SN candidates and obtained spectroscopicredshifts of 7,000 host galaxies. Based on the light-curve quality, we select1635 photometrically-identified SNe Ia with spectroscopic redshift 0.10$< z<$1.13, which is the largest sample of supernovae from any single survey andincreases the number of known $z>0.5$ supernovae by a factor of five. In acompanion paper, we present cosmological results of the DES-SN sample combinedwith 194 spectroscopically-classified SNe Ia at low redshift as an anchor forcosmological fits. Here we present extensive modeling of this combined sampleand validate the entire analysis pipeline used to derive distances. We showthat the statistical and systematic uncertainties on cosmological parametersare $\sigma_{\Omega_M,{\rm stat+sys}}^{\Lambda{\rm CDM}}=$0.017 in a flat$\Lambda$CDM model, and $(\sigma_{\Omega_M},\sigma_w)_{\rm stat+sys}^{w{\rmCDM}}=$(0.082, 0.152) in a flat $w$CDM model. Combining the DES SN data withthe highly complementary CMB measurements by Planck Collaboration (2020)reduces uncertainties on cosmological parameters by a factor of 4. In allcases, statistical uncertainties dominate over systematics. We show thatuncertainties due to photometric classification make up less than 10% of thetotal systematic uncertainty budget. This result sets the stage for the nextgeneration of SN cosmology surveys such as the Vera C. Rubin Observatory'sLegacy Survey of Space and Time.

The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties