The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset (2401.02929v3)

Abstract: We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being a SN Ia, we find 1635 DES SNe in the redshift range $0.10<z\<1.13$ that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality $z\>0.5$ SNe compared to the previous leading compilation of Pantheon+, and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints we combine the DES supernova data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning $0.025<z<0.10$. Using SN data alone and including systematic uncertainties we find $\Omega_{\rm M}=0.352\pm 0.017$ in flat $\Lambda$CDM. Supernova data alone now require acceleration ($q_0<0$ in $\Lambda$CDM) with over $5\sigma$ confidence. We find $(\Omega_{\rm M},w)=(0.264^{+0.074}{-0.096},-0.80^{+0.14}{-0.16})$ in flat $w$CDM. For flat $w_0w_a$CDM, we find $(\Omega_{\rm M},w_0,w_a)=(0.495^{+0.033}{-0.043},-0.36^{+0.36}{-0.30},-8.8^{+3.7}_{-4.5})$. Including Planck CMB data, SDSS BAO data, and DES $3\times2$-point data gives $(\Omega_{\rm M},w)=(0.321\pm0.007,-0.941\pm0.026)$. In all cases dark energy is consistent with a cosmological constant to within $\sim2\sigma$. In our analysis, systematic errors on cosmological parameters are subdominant compared to statistical errors; paving the way for future photometrically classified supernova analyses.

• The paper introduces a novel machine learning method to classify approximately 1500 high-redshift Type Ia supernovae for improved cosmological analysis.
• It presents a matter density parameter of ΩM = 0.352 ± 0.017 and robust evidence for cosmic acceleration with q₀ < 0 over 5σ confidence.
• It demonstrates that photometric classification can yield reliable results, setting a precedent for future surveys integrating diverse cosmological datasets.

Insights into the Dark Energy Survey: Supernova Cosmology Results

The recent publication of the Dark Energy Survey (DES) cosmology results leverages the vast dataset collected over a five-year period to enhance our understanding of the universe's expansion through the paper of Type Ia supernovae (SNe Ia). This work represents a comprehensive effort to analyze approximately 1500 high-redshift supernovae, significantly expanding on previous datasets and providing one of the most precise constraints on cosmological parameters to date.

The DES approach utilized in the analysis marks a significant shift from traditional methods. Instead of solely relying on spectroscopically confirmed supernovae, this paper introduces a machine learning algorithm to classify supernovae based on their light curves in four photometric bands. This innovative classification method, coupled with spectroscopic redshift data obtained from dedicated follow-up of the host galaxies, underpins the robustness of the findings.

Key Results and Their Implications

The paper reveals several noteworthy results. Firstly, the DES team's analysis indicates a matter density parameter, $\Omega_{\rm M}$, to be $0.352 \pm 0.017$ in the context of flat-$\Lambda$CDM cosmology, showcasing unprecedented precision. Moreover, when factoring in supernova data alone, the findings firmly support cosmological acceleration, illustrating $q_0 < 0$ with over 5$\sigma$ confidence. This underscores the prevailing concept of dark energy driving the accelerated expansion of the universe.

Interestingly, the paper illuminates a preference for a dark energy equation of state with parameters $$(\Omega_{\rm M}, w) = (0.264^{+0.074}_{-0.096}, -0.80^{+0.14}_{-0.16})$$ in flat-$w$CDM models. The consistency of these parameters with a cosmological constant, within approximately 2$\sigma$, provides a tentative yet insightful confirmation of dark energy's role and nature. When incorporating additional datasets such as Planck CMB, SDSS BAO, and DES 3x2-point data, they report $$(\Omega_{\rm M}, w) = (0.321 \pm 0.007, -0.941 \pm 0.026)$$, lending further support to these conclusions.

Systematic errors within this comprehensive paper were found to be subdominant to statistical errors, highlighting the reliability of the DES photometrically classified supernovae for cosmological analyses. This aspect sets a precedent for future research, suggesting that forthcoming photometric surveys could continue to refine our understanding with minimal impact from systematic uncertainties.

Pathways for Future Exploration

The implications of this research extend well beyond the specific numerical results. The use of machine learning for supernova classification and the extensive dataset from DES opens avenues for even more detailed investigations into the mysterious dark energy. Future explorations can build upon this framework, leveraging larger datasets and enhanced computational methods to further constrain the dynamics of dark energy, potentially exploring beyond the $\Lambda$CDM paradigm.

Moreover, these findings are integral to the cosmology community's ongoing efforts to reconcile the cosmic expansion history with observations of the cosmic microwave background and other large-scale structures. As further studies expand into higher precision measurements, the integration of diverse datasets will be critical in resolving ongoing debates such as the Hubble tension.

The DES contribution stands as a testament to the power of collaboration, innovation, and the strategic application of machine learning within astronomical research. It lays a foundational path for the next generation of cosmologists, poised to tackle the remaining mysteries of our universe with ever-increasing precision.