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Abstract We present a simulation of the time-domain catalog for the Nancy Grace Roman Space Telescope’s High-Latitude Time-Domain Core Community Survey. This simulation, called the Hourglass simulation, uses the most up-to-date spectral energy distribution models and rate measurements for 10 extragalactic time-domain sources. We simulate these models through the design reference Roman Space Telescope survey: four filters per tier, a five-day cadence, over 2 yr, a wide tier of 19 deg², and a deep tier of 4.2 deg², with ∼20% of those areas also covered with prism observations. We find that a science-independent Roman time-domain catalog, assuming a signal-to-noise ratio at a max of >5, would have approximately 21,000 Type Ia supernovae, 40,000 core-collapse supernovae, around 70 superluminous supernovae, ∼35 tidal disruption events, three kilonovae, and possibly pair-instability supernovae. In total, Hourglass has over 64,000 transient objects, 11,000,000 photometric observations, and 500,000 spectra. Additionally, Hourglass is a useful data set to train machine learning classification algorithms. We show that SCONE is able to photometrically classify Type Ia supernovae with high precision (∼95%) to az> 2. Finally, we present the first realistic simulations of non-Type Ia supernovae spectral time series data from Roman’s prism. 

Aims.TheVera C. RubinObservatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. Methods.We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg²region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. Results.We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models. 

ABSTRACT Recent cosmological analyses measuring distances of type Ia supernovae (SNe Ia) and baryon acoustic oscillations (BAO) have all given similar hints at time-evolving dark energy. To examine whether underestimated SN Ia systematics might be driving these results, Efstathiou (2025) compared overlapping SN events between Pantheon+ and DES-SN5YR (20 per cent SNe are in common), and reported evidence for an $$\sim$$0.04 mag offset between the low- and high-redshift distance measurements of this subsample of events. If this offset is arbitrarily subtracted from the entire DES-SN5YR sample, the preference for evolving dark energy is reduced. In this paper, we show that this offset is mostly due to different corrections for Malmquist bias between the two samples; therefore, an object-to-object comparison can be misleading. Malmquist bias corrections differ between the two analyses for several reasons. First, DES-SN5YR used an improved model of SN Ia luminosity scatter compared to Pantheon+ but the associated scatter-model uncertainties are included in the error budget. Secondly, improvements in host mass estimates in DES-SN5YR also affected SN standardized magnitudes and their bias corrections. Thirdly, and most importantly, the selection functions of the two compilations are significantly different, hence the inferred Malmquist bias corrections. Even if the original scatter model and host properties from Pantheon+ are used instead, the evidence for evolving dark energy from CMB, DESI BAO Year 1 and DES-SN5YR is only reduced from 3.9$$\sigma$$ to 3.3$$\sigma$$, consistent with the error budget. Finally, in this investigation, we identify an underestimated systematic uncertainty related to host galaxy property uncertainties, which could increase the final DES-SN5YR error budget by 3 per cent. In conclusion, we confirm the validity of the published DES-SN5YR results. 

ABSTRACT Gravitational lensing magnification of Type Ia supernovae (SNe Ia) allows information to be obtained about the distribution of matter on small scales. In this paper, we derive limits on the fraction $$\alpha$$ of the total matter density in compact objects (which comprise stars, stellar remnants, small stellar groupings, and primordial black holes) of mass M > 0.03 $${\rm M}_{\odot }$$ over cosmological distances. Using 1532 SNe Ia from the Dark Energy Survey Year 5 sample (DES-SN5YR) combined with a Bayesian prior for the absolute magnitude M, we obtain α < 0.12 at the 95 per cent confidence level after marginalization over cosmological parameters, lensing due to large-scale structure, and intrinsic non-Gaussianity. Similar results are obtained using priors from the cosmic microwave background, baryon acoustic oscillations, and galaxy weak lensing, indicating our results do not depend on the background cosmology. We argue our constraints are likely to be conservative (in the sense of the values we quote being higher than the truth), but discuss scenarios in which they could be weakened by systematics of the order of $$\Delta \alpha \sim 0.04$$. 

ABSTRACT We measure the current expansion rate of the Universe, Hubble’s constant $$H_0$$, by calibrating the absolute magnitudes of supernovae to distances measured by baryon acoustic oscillations (BAO). This ‘inverse distance ladder’ technique provides an alternative to calibrating supernovae using nearby absolute distance measurements, replacing the calibration with a high-redshift anchor. We use the recent release of 1829 supernovae from the Dark Energy Survey spanning $$0.01\lt z\lt 1.13$$ anchored to the recent baryon acoustic oscillation measurements from Dark Energy Spectroscopic Instrument (DESI) spanning $$0.30 \lt z_{\mathrm{eff}}\lt 2.33$$. To trace cosmology to $z=0$, we use the third-, fourth-, and fifth-order cosmographic models, which, by design, are agnostic about the energy content and expansion history of the universe. With the inclusion of the higher redshift DESI-BAO data, the third-order model is a poor fit to both data sets, with the fourth-order model being preferred by the Akaike Information Criterion. Using the fourth-order cosmographic model, we find $$H_0=67.19^{+0.66}_{-0.64}\mathrm{~km} \mathrm{~s}^{-1} \mathrm{~Mpc}^{-1}$$, in agreement with the value found by Planck without the need to assume Flat-$$\Lambda$$CDM. However, the best-fitting expansion history differs from that of Planck, providing continued motivation to investigate these tensions. 

ABSTRACT Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure the distance–redshift relation. While obtaining a host-galaxy spectroscopic redshift for most SNe is feasible for small-area transient surveys, it will be too resource intensive for upcoming large-area surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time, which will observe on the order of millions of SNe. Here, we use data from the Dark Energy Survey (DES) to address this problem with photometric redshifts (photo-z) inferred directly from the SN light curve in combination with Gaussian and full $p(z)$ priors from host-galaxy photo-z estimates. Using the DES 5-yr photometrically classified SN sample, we consider several photo-z algorithms as host-galaxy photo-z priors, including the Self-Organizing Map redshifts (SOMPZ), Bayesian Photometric Redshifts (BPZ), and Directional-Neighbourhood Fitting (DNF) redshift estimates employed in the DES 3 × 2 point analyses. With detailed catalogue-level simulations of the DES 5-yr sample, we find that the simulated w can be recovered within $$\pm 0.02$$ when using SN+SOMPZ or DNF prior photo-z, smaller than the average statistical uncertainty for these samples of 0.03. With data, we obtain biases in w consistent with simulations within $${\sim} 1\sigma$$ for three of the five photo-z variants. We further evaluate how photo-z systematics interplay with photometric classification and find classification introduces a subdominant systematic component. This work lays the foundation for next-generation fully photometric SNe Ia cosmological analyses. 

ABSTRACT Current and future Type Ia Supernova (SN Ia) surveys will need to adopt new approaches to classifying SNe and obtaining their redshifts without spectra if they wish to reach their full potential. We present here a novel approach that uses only photometry to identify SNe Ia in the 5-yr Dark Energy Survey (DES) data set using the SuperNNova classifier. Our approach, which does not rely on any information from the SN host-galaxy, recovers SNe Ia that might otherwise be lost due to a lack of an identifiable host. We select $$2{,}298$$ high-quality SNe Ia from the DES 5-yr data set an almost complete sample of detected SNe Ia. More than 700 of these have no spectroscopic host redshift and are potentially new SNIa compared to the DES-SN5YR cosmology analysis. To analyse these SNe Ia, we derive their redshifts and properties using only their light curves with a modified version of the SALT2 light-curve fitter. Compared to other DES SN Ia samples with spectroscopic redshifts, our new sample has in average higher redshift, bluer and broader light curves, and fainter host-galaxies. Future surveys such as LSST will also face an additional challenge, the scarcity of spectroscopic resources for follow-up. When applying our novel method to DES data, we reduce the need for follow-up by a factor of four and three for host-galaxy and live SN, respectively, compared to earlier approaches. Our novel method thus leads to better optimization of spectroscopic resources for follow-up. 

Abstract We present the full Hubble diagram of photometrically classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7000 host galaxies. Based on the light-curve quality, we select 1635 photometrically identified SNe Ia with spectroscopic redshift 0.10 <z< 1.13, which is the largest sample of supernovae from any single survey and increases the number of knownz> 0.5 supernovae by a factor of 5. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are ${\sigma }_{{\mathrm{\Omega }}_M,\mathrm{stat}+\mathrm{sys}}^{\mathrm{\Lambda }\mathrm{CDM}}=$0.017 in a flat ΛCDM model, and $({\sigma }_{{\mathrm{\Omega }}_M},{\sigma }_w{)}_{\mathrm{stat}+\mathrm{sys}}^{w\mathrm{CDM}}$= (0.082, 0.152) in a flatwCDM model. Combining the DES SN data with the highly complementary cosmic microwave background measurements by Planck Collaboration reduces by a factor of 4 uncertainties on cosmological parameters. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time. 

Abstract We presentgrizphotometric light curves for the full 5 yr of the Dark Energy Survey Supernova (DES-SN) program, obtained with both forced point-spread function photometry on difference images (DiffImg) performed during survey operations, and scene modelling photometry (SMP) on search images processed after the survey. This release contains 31,636DiffImgand 19,706 high-quality SMP light curves, the latter of which contain 1635 photometrically classified SNe that pass cosmology quality cuts. This sample spans the largest redshift (z) range ever covered by a single SN survey (0.1 <z< 1.13) and is the largest single sample from a single instrument of SNe ever used for cosmological constraints. We describe in detail the improvements made to obtain the final DES-SN photometry and provide a comparison to what was used in the 3 yr DES-SN spectroscopically confirmed Type Ia SN sample. We also include a comparative analysis of the performance of the SMP photometry with respect to the real-timeDiffImgforced photometry and find that SMP photometry is more precise, more accurate, and less sensitive to the host-galaxy surface brightness anomaly. The public release of the light curves and ancillary data can be found atgithub.com/des-science/DES-SN5YRand doi:10.5281/zenodo.12720777.