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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.

 

Context.The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches is machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs.

Aims.The goal of this work is to calculate the photo-zsfor the Year 3 (Y3) Dark Energy Survey (DES) Deep Fields catalogue using the Directional Neighborhood Fitting (DNF) machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained by DNF on the Y3 DES Deep Fields catalogue with that of the EAzY template fitting approach.

Methods.We emulated – at a brighter magnitude – the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We used a principal component analysis to graphically assess the incompleteness and relate it with the performance parameters provided by DNF. Finally, we applied the results on the incompleteness to the photo-zcomputation on the Y3 DES Deep Fields with DNF and estimated its performance.

Results.The photo-zsof the galaxies in the DES deep fields were computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. We have developed some techniques to evaluate the performance in the absence of “true” redshift and to assess the completeness. We have studied the tradeoff in the training sample between the highest spectroscopic redshift quality versus completeness. We found some advantages in relaxing the highest-quality spectroscopic redshift requirements at fainter magnitudes in favour of completeness. The results achieved by DNF on the Y3 Deep Fields are competitive with the ones provided by EAzY, showing notable stability at high redshifts. It should be noted that the good results obtained by DNF in the estimation of photo-zsin deep field catalogues make DNF suitable for the future Legacy Survey of Space and Time (LSST) andEucliddata, which will have similar depths to the Y3 DES Deep Fields.

 

We present a spectroscopic analysis of Eridanus IV (Eri IV) and Centaurus I (Cen I), two ultrafaint dwarf galaxies of the Milky Way. Using IMACS/Magellan spectroscopy, we identify 28 member stars of Eri IV and 34 member stars of Cen I. For Eri IV, we measure a systemic velocity of$v_{\mathrm{sys}}=-{31.5}_{-1.2}^{+1.3}\mathrm{km}{\mathrm{s}}^{-1}$, and velocity dispersion${\sigma }_v={6.1}_{-0.9}^{+1.2}\mathrm{km}{\mathrm{s}}^{-1}$. Additionally, we measure the metallicities of 16 member stars of Eri IV. We find a metallicity of$[\mathrm{Fe}/\mathrm{H}]=-{2.87}_{-0.07}^{+0.08}$, and resolve a dispersion ofσ_[Fe/H]=0.20 ± 0.09. The mean metallicity is marginally lower than all other known ultrafaint dwarf galaxies, making it one of the most metal-poor galaxies discovered thus far. Eri IV also has a somewhat unusual right-skewed metallicity distribution. For Cen I, we find a velocityv_sys= 44.9 ± 0.8 km s⁻¹, and velocity dispersion${\sigma }_v={4.2}_{-0.5}^{+0.6}\mathrm{km}{\mathrm{s}}^{-1}$. We measure the metallicities of 27 member stars of Cen I, and find a mean metallicity [Fe/H] = −2.57 ± 0.08, and metallicity dispersion${\sigma }_{[\mathrm{Fe}/\mathrm{H}]}={0.38}_{-0.05}^{+0.07}$. We calculate the systemic proper motion, orbit, and the astrophysical J-factor for each system, the latter of which indicates that Eri IV is a good target for indirect dark matter detection. We also find no strong evidence for tidal stripping of Cen I or Eri IV. Overall, our measurements confirm that Eri IV and Cen I are dark-matter-dominated galaxies with properties largely consistent with other known ultrafaint dwarf galaxies. The low metallicity, right-skewed metallicity distribution, and high J-factor make Eri IV an especially interesting candidate for further follow-up.

 

Abstract We present the discovery of DELVE 6, an ultra-faint stellar system identified in the second data release of the DECam Local Volume Exploration (DELVE) survey. Based on a maximum-likelihood fit to its structure and stellar population, we find that DELVE 6 is an old ( τ > 9.8 Gyr at 95% confidence) and metal-poor ([Fe/H] < −1.17 dex at 95% confidence) stellar system with an absolute magnitude of M V = − 1.5 − 0.6 + 0.4 mag and an azimuthally averaged half-light radius of r 1 / 2 = 10 − 3 + 4 pc. These properties are consistent with the population of ultra-faint star clusters uncovered by recent surveys. Interestingly, DELVE 6 is located at an angular separation of ∼10° from the center of the Small Magellanic Cloud (SMC), corresponding to a 3D physical separation of ∼20 kpc given the system’s observed distance ( D ⊙ = 80 kpc). This also places the system ∼35 kpc from the center of the Large Magellanic Cloud (LMC), lying within recent constraints on the size of the LMC’s dark matter halo. We tentatively measure the proper motion of DELVE 6 using data from Gaia, which we find supports a potential association between the system and the LMC/SMC. Although future kinematic measurements will be necessary to determine its origins, we highlight that DELVE 6 may represent only the second or third ancient ( τ > 9 Gyr) star cluster associated with the SMC, or one of fewer than two dozen ancient clusters associated with the LMC. Nonetheless, we cannot currently rule out the possibility that the system is a distant Milky Way halo star cluster. 

Reverberation mapping is the leading technique used to measure direct black hole masses outside of the local Universe. Additionally, reverberation measurements calibrate secondary mass-scaling relations used to estimate single-epoch virial black hole masses. The Australian Dark Energy Survey (OzDES) conducted one of the first multi-object reverberation mapping surveys, monitoring 735 AGN up to z ∼ 4, over 6 years. The limited temporal coverage of the OzDES data has hindered recovery of individual measurements for some classes of sources, particularly those with shorter reverberation lags or lags that fall within campaign season gaps. To alleviate this limitation, we perform a stacking analysis of the cross-correlation functions of sources with similar intrinsic properties to recover average composite reverberation lags. This analysis leads to the recovery of average lags in each redshift-luminosity bin across our sample. We present the average lags recovered for the Hβ, Mg ii, and C iv samples, as well as multiline measurements for redshift bins where two lines are accessible. The stacking analysis is consistent with the Radius–Luminosity relations for each line. Our results for the Hβ sample demonstrate that stacking has the potential to improve upon constraints on the R–L relation, which have been derived only from individual source measurements until now.

 

We report results from a systematic wide-area search for faint dwarf galaxies at heliocentric distances from 0.3 to 2 Mpc using the full 6 yr of data from the Dark Energy Survey (DES). Unlike previous searches over the DES data, this search specifically targeted a field population of faint galaxies located beyond the Milky Way virial radius. We derive our detection efficiency for faint, resolved dwarf galaxies in the Local Volume with a set of synthetic galaxies and expect our search to be complete to M _V ∼ (‑7, ‑10) mag for galaxies at D = (0.3, 2.0) Mpc. We find no new field dwarfs in the DES footprint, but we report the discovery of one high-significance candidate dwarf galaxy at a distance of ${2.2}_{-0.12}^{+0.05}\,\mathrm{Mpc}$ , a potential satellite of the Local Volume galaxy NGC 55, separated by 47' (physical separation as small as 30 kpc). We estimate this dwarf galaxy to have an absolute V-band magnitude of $-{8.0}_{-0.3}^{+0.5}\,\mathrm{mag}$ and an azimuthally averaged physical half-light radius of ${2.2}_{-0.4}^{+0.5}\,\mathrm{kpc}$ , making this one of the lowest surface brightness galaxies ever found with $\mu =32.3\,\mathrm{mag}\,{\mathrm{arcsec}}^{-2}$. This is the largest, most diffuse galaxy known at this luminosity, suggesting possible tidal interactions with its host.