Search for: All records

ABSTRACT Samples of young Type Ia supernovae have shown ‘early excess’ emission in a few cases. Similar excesses are predicted by some explosion and progenitor scenarios and hence can provide important clues regarding the origin of thermonuclear supernovae. They are, however, only predicted to last up to the first few days following explosion. It is therefore unclear whether such scenarios are intrinsically rare or whether the relatively small sample size simply reflects the difficulty in obtaining sufficiently early detections. To that end, we perform toy simulations covering a range of survey depths and cadences, and investigate the efficiency with which young Type Ia supernovae are recovered. As input for our simulations, we use models that broadly cover the range of predicted luminosities. Based on our simulations, we find that in a typical 3 d cadence survey, only ∼10 per cent of Type Ia supernovae would be detected early enough to rule out the presence of an excess. A 2 d cadence, however, should see this increase to ∼15 per cent. We find comparable results from more detailed simulations of the Zwicky Transient Facility surveys. Using the recovery efficiencies from these detailed simulations, we investigate the number of young Type Ia supernovae expected to be discovered assuming some fraction of the population comes from scenarios producing an excess at early times. Comparing the results of our simulations to observations, we find that the intrinsic fraction of Type Ia supernovae with early flux excesses is $$\sim 28^{+13}_{-11}{{\ \rm per\ cent}}$$. 

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 present the photometric and spectroscopic evolution of supernova (SN) 2019cad during the first ∼100 d from explosion. Based on the light-curve morphology, we find that SN 2019cad resembles the double-peaked Type Ib/c SN 2005bf and the Type Ic PTF11mnb. Unlike those two objects, SN 2019cad also shows the initial peak in the redder bands. Inspection of the g-band light curve indicates the initial peak is reached in ∼8 d, while the r-band peak occurred ∼15 d post-explosion. A second and more prominent peak is reached in all bands at ∼45 d past explosion, followed by a fast decline from ∼60 d. During the first 30 d, the spectra of SN 2019cad show the typical features of a Type Ic SN, however, after 40 d, a blue continuum with prominent lines of Si ii λ6355 and C ii λ6580 is observed again. Comparing the bolometric light curve to hydrodynamical models, we find that SN 2019cad is consistent with a pre-SN mass of 11 M⊙, and an explosion energy of 3.5 × 1051 erg. The light-curve morphology can be reproduced either by a double-peaked 56Ni distribution with an external component of 0.041 M⊙, and an internal component of 0.3 M⊙ or a double-peaked 56Ni distribution plus magnetar model (P ∼ 11 ms and B ∼ 26 × 1014 G). If SN 2019cad were to suffer from significant host reddening (which cannot be ruled out), the 56Ni model would require extreme values, while the magnetar model would still be feasible. 

Abstract We present cosmological constraints from the sample of Type Ia supernovae (SNe Ia) discovered and measured during the full 5 yr of the Dark Energy Survey (DES) SN program. In contrast to most previous cosmological samples, in which SNe 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 an 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-qualityz> 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 SN 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 Ω_M= 0.352 ± 0.017 in flat ΛCDM. SN data alone now require acceleration (q₀< 0 in ΛCDM) with over 5σconfidence. We find $({\mathrm{\Omega }}_{\mathrm{M}},w)=({0.264}_{-0.096}^{+0.074},-{0.80}_{-0.16}^{+0.14})$in flatwCDM. For flatw₀w_aCDM, we find $({\mathrm{\Omega }}_{\mathrm{M}},w_0,w_a)=({0.495}_{-0.043}^{+0.033},-{0.36}_{-0.30}^{+0.36},-{8.8}_{-4.5}^{+3.7})$, consistent with a constant equation of state to within ∼2σ. Including Planck cosmic microwave background, Sloan Digital Sky Survey baryon acoustic oscillation, and DES 3 × 2pt data gives (Ω_M,w) = (0.321 ± 0.007, −0.941 ± 0.026). In all cases, dark energy is consistent with a cosmological constant to within ∼2σ. Systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified SN analyses. 

ABSTRACT As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multiband light curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNovatrained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1863 SNe Ia from which we select 1484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time. 

ABSTRACT Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of ‘contamination’ from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such ‘non-Ia’ contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7–99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC (‘BEAMS with Bias Correction’), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet’s criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015–0.03). Finally, we measure biases due to contamination on w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample. 

ABSTRACT Rapidly evolving transients (RETs), also termed fast blue optical transients, are a recently discovered group of astrophysical events that display rapid luminosity evolution. RETs typically rise to peak in less than 10 d and fade within 30, a time-scale unlikely to be compatible with the decay of Nickel-56 that drives conventional supernovae (SNe). Their peak luminosity spans a range of −15 < Mg < −22.5, with some events observed at redshifts greater than 1. Their evolution on fast time-scales has hindered high-quality follow-up observations, and thus their origin and explosion/emission mechanism remains unexplained. In this paper, we present the largest sample of RETs to date, comprising 106 objects discovered by the Dark Energy Survey, and perform the most comprehensive analysis of RET host galaxies. Using deep-stacked photometry and emission lines from OzDES spectroscopy, we derive stellar masses and star formation rates (SFRs) for 49 host galaxies, and metallicities ([O/H]) for 37. We find that RETs explode exclusively in star-forming galaxies and are thus likely associated with massive stars. Comparing RET hosts to samples of host galaxies of other explosive transients as well as field galaxies, we find that RETs prefer galaxies with high specific SFRs (〈log (sSFR)〉 ∼ −9.6), indicating a link to young stellar populations, similar to stripped-envelope SNe. RET hosts appear to show a lack of chemical enrichment, their metallicities akin to long-duration gamma-ray bursts and superluminous SN host galaxies (〈12 + log (O/H)〉 ∼ 9.4). There are no clear relationships between mass or SFR of the host galaxies and the peak magnitudes or decline rates of the transients themselves. 

Abstract On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star–black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera on the 4 m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on eight separate nights to observe 11 candidates using the 4.1 m Southern Astrophysical Research (SOAR) telescope’s Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond. 

ABSTRACT The 5-yr Dark Energy Survey Supernova Programme (DES-SN) is one of the largest and deepest transient surveys to date in terms of volume and number of supernovae. Identifying and characterizing the host galaxies of transients plays a key role in their classification, the study of their formation mechanisms, and the cosmological analyses. To derive accurate host galaxy properties, we create depth-optimized coadds using single-epoch DES-SN images that are selected based on sky and atmospheric conditions. For each of the five DES-SN seasons, a separate coadd is made from the other four seasons such that each SN has a corresponding deep coadd with no contaminating SN emission. The coadds reach limiting magnitudes of order ∼27 in g band, and have a much smaller magnitude uncertainty than the previous DES-SN host templates, particularly for faint objects. We present the resulting multiband photometry of host galaxies for samples of spectroscopically confirmed type Ia (SNe Ia), core-collapse (CCSNe), and superluminous (SLSNe) as well as rapidly evolving transients (RETs) discovered by DES-SN. We derive host galaxy stellar masses and probabilistically compare stellar-mass distributions to samples from other surveys. We find that the DES spectroscopically confirmed sample of SNe Ia selects preferentially fewer high-mass hosts at high-redshift compared to other surveys, while at low redshift the distributions are consistent. DES CCSNe and SLSNe hosts are similar to other samples, while RET hosts are unlike the hosts of any other transients, although these differences have not been disentangled from selection effects.