# The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset

**Authors:**  The Dark Energy Survey Collaboration, T. M. C. Abbott, M. Acevedo, M. Aguena, A. Alarcon, S. Allam, O. Alves, A. Amon, F. Andrade-Oliveira, J. Annis, P. Armstrong, J. Asorey, S. Avila, D. Bacon, B. Bassett, K. Bechtol, P. H. Bernardinelli, G. M. Bernstein, E. Bertin, J. Blazek  
**arXiv:** [2401.02929](https://arxiv.org/abs/2401.02929)  
**Date:** 2024-01-05  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2401.02929v1/

## Key points

- The Dark Energy Survey (DES) Supernova Program revolutionized our understanding of the universe by discovering and classifying Type Ia supernovae (SN Ia) over a five-year period.
- The DES team used a machine learning algorithm to classify SNe based on their light curves in four photometric bands, resulting in the identification of 1635 high-quality DES SN in the redshift range $0.10<z<1.13$.
- This expanded sample size allowed for the tightest cosmological constraints ever achieved by a SN dataset, quintupling the number of high-quality SNe at $z>0.5$ compared to previous compilations.
- By combining DES supernova data with an external low-redshift sample, the team derived key cosmological parameters: $\Omega_{\rm M}=0.352\pm 0.017$ in a flat $\Lambda$CDM model using SN data alone, among other results.
- Incorporating additional data from Planck CMB observations, SDSS BAO measurements, and DES $3\times2$-point data led to refined cosmological parameters: $(\Omega_{\rm M},w)=(0.314^{+0.016}_{-0.017},-1.00^{+0.12}_{-0.13})$, showing consistency with dark energy as a cosmological constant within statistical uncertainties across various models tested in this study.

## Summary

The Dark Energy Survey (DES) Supernova Program has revolutionized our understanding of the universe by discovering and classifying Type Ia supernovae (SN Ia) over a five-year period. Unlike previous studies that relied on spectroscopic classifications, the DES team utilized a machine learning algorithm to classify SNe based on their light curves in four photometric bands. This innovative approach, combined with spectroscopic redshift data from host galaxies, resulted in the identification of 1635 high-quality DES SN in the redshift range $0.10<z<1.13$. This significantly expanded sample size allowed for the tightest cosmological constraints ever achieved by a SN dataset, quintupling the number of high-quality SNe at $z>0.5$ compared to previous compilations. By combining the DES supernova data with a high-quality external low-redshift sample spanning $0.025<z<0.10$, consisting of 194 SNe Ia, the team derived key cosmological parameters. In a flat $\Lambda$CDM model using SN data alone and accounting for systematic uncertainties, they found $\Omega_{\rm M}=0.352\pm 0.017$. In a flat $w$CDM model, the results were $(\Omega_{\rm M},w)=(0.264^{+0.074}_{-0.096},-0.80^{+0.14}_{-0.16})$. For a flat $w_0w_a$CDM model, they obtained $(\Omega_{\rm M},w_0,w_a)=(0.495^{+0.033}_{-0.043},-0.36^{+0.36}_{-0
    .30},-8 ... Furthermore, incorporating additional data from Planck CMB observations, SDSS BAO measurements, and DES $3\times2$-point data led to refined cosmological parameters: $(\Omega_{\rm M},w)=(0.314^{+0.016}_{-0.017},-1.00^{+0.12}_{-0.13})$. Overall, these results demonstrate that dark energy is consistent with a cosmological constant within statistical uncertainties across various models tested in this study.