We present a spectroscopic analysis of Type I superluminous supernova (SLSN-I), SN 2018bsz. While it closely resembles SLSNe-I, the multi-component H\(\alpha\) line appearing at \(\sim30\) d ...post-maximum is the most atypical. The H\(\alpha\) is characterised by two emission components, one at \(+3000\) km/s and a second at \(-7500\) km/s, with a third, near-zero velocity component appearing after a delay. The blue and central components can be described by Gaussian profiles of intermediate width, but the red component is significantly broader and Lorentzian. The blue component evolves towards lower velocity before fading at \(100\) d post-peak, concurrently with a light curve break. Multi-component profiles are observed in other hydrogen lines including Pa\(\beta\), and in lines of Ca II and He I. Spectropolarimetry obtained before (10.2 d) and after (38.4 d) the appearance of the H lines show a large shift on the Stokes \(Q\) -- \(U\) plane consistent with SN 2018bsz undergoing radical changes in its geometry. Assuming the SN is almost unpolarised at 10.2 d, the continuum polarisation at 38.4 d reaches \(P \sim1.8\%\) implying a highly asymmetric configuration. We propose that the observed evolution of SN 2018bsz can be explained by highly aspherical CSM. After the SN explosion, the CSM is quickly overtaken by the ejecta, but as the photosphere starts to recede, the different CSM regions re-emerge producing the peculiar line profiles. Based on the first appearance of H\(\alpha\), we can constrain the distance of the CSM to be less than \(430\) AU, or even lower (\(<87\) AU) if the pre-peak plateau is related to an eruption that created the CSM. The presence of CSM has been inferred for other SLSNe-I. However, it is not clear whether the rare properties of SN 2018bsz can be generalised for SLSNe-I or whether they are the result of an uncommon evolutionary path, possibly involving a binary companion.
The design and analysis of time-domain sky surveys requires the ability to simulate accurately realistic populations of core collapse supernova (SN) events. We present a set of spectral time-series ...templates designed for this purpose, for both hydrogen-rich (type II, IIn, IIb) and stripped envelope (types Ib, Ic, Ic-BL) core collapse supernovae. We use photometric and spectroscopic data for 67 core collapse supernovae from the literature, and for each generate a time-series spectral template. The techniques used to build the templates are fully data-driven with no assumption of any parametric form or model for the light curves. The template-building code is open-source, and can be applied to any transient for which well-sampled multi-band photometry and multiple spectroscopic observations are available. We extend these spectral templates into the near-ultraviolet to \(\lambda \lambda \sim 1600 \AA\) using observer-frame ultraviolet photometry. We also provide a set of templates corrected for host galaxy dust extinction, and provide a set of luminosity functions that can be used with our spectral templates in simulations. We give an example of how these templates can be used by integrating them within the popular SN simulation package \(\textsc{snana}\), and simulating core collapse supernovae in photometrically-selected cosmological type Ia supernova samples, prone to contamination from core collapse events.
We present the luminosity functions and host galaxy properties of the Dark Energy Survey (DES) core-collapse supernova (CCSN) sample, consisting of 69 Type II and 50 Type Ibc spectroscopically and ...photometrically-confirmed supernovae over a redshift range \(0.045<z<0.25\). We fit the observed DES \(griz\) CCSN light-curves and K-correct to produce rest-frame \(R\)-band light curves. We compare the sample with lower-redshift CCSN samples from Zwicky Transient Facility (ZTF) and Lick Observatory Supernova Search (LOSS). Comparing luminosity functions, the DES and ZTF samples of SNe II are brighter than that of LOSS with significances of 3.0\(\sigma\) and 2.5\(\sigma\) respectively. While this difference could be caused by redshift evolution in the luminosity function, simpler explanations such as differing levels of host extinction remain a possibility. We find that the host galaxies of SNe II in DES are on average bluer than in ZTF, despite having consistent stellar mass distributions. We consider a number of possibilities to explain this -- including galaxy evolution with redshift, selection biases in either the DES or ZTF samples, and systematic differences due to the different photometric bands available -- but find that none can easily reconcile the differences in host colour between the two samples and thus its cause remains uncertain.
Recent analyses have found intriguing correlations between the colour (\(c\)) of type Ia supernovae (SNe Ia) and the size of their 'mass-step', the relationship between SN Ia host galaxy stellar mass ...(\(M_\mathrm{stellar}\)) and SN Ia Hubble residual, and suggest that the cause of this relationship is dust. Using 675 photometrically-classified SNe Ia from the Dark Energy Survey 5-year sample, we study the differences in Hubble residual for a variety of global host galaxy and local environmental properties for SN Ia subsamples split by their colour. We find a \(3\sigma\) difference in the mass-step when comparing blue (\(c<0\)) and red (\(c>0\)) SNe. We observe the lowest r.m.s. scatter (\(\sim0.14\) mag) in the Hubble residual for blue SNe in low mass/blue environments, suggesting that this is the most homogeneous sample for cosmological analyses. By fitting for \(c\)-dependent relationships between Hubble residuals and \(M_\mathrm{stellar}\), approximating existing dust models, we remove the mass-step from the data and find tentative \(\sim 2\sigma\) residual steps in rest-frame galaxy \(U-R\) colour. This indicates that dust modelling based on \(M_\mathrm{stellar}\) may not fully explain the remaining dispersion in SN Ia luminosity. Instead, accounting for a \(c\)-dependent relationship between Hubble residuals and global \(U-R\), results in \(\leq1\sigma\) residual steps in \(M_\mathrm{stellar}\) and local \(U-R\), suggesting that \(U-R\) provides different information about the environment of SNe Ia compared to \(M_\mathrm{stellar}\), and motivating the inclusion of galaxy \(U-R\) colour in SN Ia distance bias correction.
We present the photometric and spectroscopic evolution of supernova (SN) 2019cad during the first \(\sim100\) days 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 \(\sim8\) days, while the r band peak occurred \(\sim15\) days post-explosion. A second and more prominent peak is reached in all bands at \(\sim45\) days past explosion, followed by and fast decline from \(\sim60\) days. During the first 30 days, the spectra of SN 2019cad show the typical features of a type Ic SN, however, after 40 days, a blue continuum with prominent lines of Si II \({\lambda}6355\) and C II \({\lambda}6580\) 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\(_{\odot}\), and an explosion energy of \(3.5\times 10^{51}\) erg. The light curve morphology can be reproduced either by a double-peaked \(^{56}\)Ni distribution with an external component of 0.041 M\(_{\odot}\) and an internal component of 0.3 M\(_{\odot}\) or a double-peaked \(^{56}\)Ni distribution plus magnetar model (P \(\sim11\) ms and B \(\sim26\times 10^{14}\) G). If SN 2019cad were to suffer from significant host reddening (which cannot be ruled out), the \(^{56}\)Ni model would require extreme values, while the magnetar model would still be feasible.
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 multi-band light-curves and host galaxy ...redshifts. For this analysis, we use the photometric classification framework SuperNNova (SNN; M\"oller et al. 2019) trained 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 1,863 SNe Ia from which we select 1,484 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 (LSST).
We present measurements of the local core collapse supernova (SN) rate using SN discoveries from the Palomar Transient Factory (PTF). We use a Monte Carlo simulation of hundreds of millions of SN ...light curve realizations coupled with the detailed PTF survey detection efficiencies to forward-model the SN rates in PTF. Using a sample of 86 core collapse SNe, including 26 stripped-envelope SNe (SESNe), we show that the overall core collapse SN volumetric rate is \(r^\mathrm{CC}_v=9.10_{-1.27}^{+1.56}\times10^{-5}\,\text{SNe yr}^{-1}\,\text{Mpc}^{-3}\, h_{70}^{3}\) at \( \langle z \rangle = 0.028\), and the SESN volumetric rate is \(r^\mathrm{SE}_v=2.41_{-0.64}^{+0.81}\times10^{-5}\, \text{SNe yr}^{-1}\,\text{Mpc}^{-3}\, h_{70}^{3}\). We further measure a volumetric rate for hydrogen-free superluminous SNe (SLSNe-I) using 8 events at \(z{\le}0.2\) of \(r^\mathrm{SLSN-I}_v=35_{-13}^{+25}\, \text{SNe yr}^{-1}\text{Gpc}^{-3}\, h_{70}^{3}\), which represents the most precise SLSN-I rate measurement to date. Using a simple cosmic star-formation history to adjust these volumetric rate measurements to the same redshift, we measure a local ratio of SLSN-I to SESN of \(\sim1/810^{+1500}_{-94}\), and of SLSN-I to all CCSN types of \(\sim 1/3500^{+2800}_{-720}\). However, using host galaxy stellar mass as a proxy for metallicity, we also show that this ratio is strongly metallicity dependent: in low-mass (\(\mathrm{log} M_{*} < 9.5 \mathrm{M}_\odot\)) galaxies, which are the only environments that host SLSN-I in our sample, we measure a SLSN-I to SESN fraction of \(1/300^{+380}_{-170}\) and \(1/1700^{+1800}_{-720}\) for all CCSN. We further investigate the SN rates a function of host galaxy stellar mass and show that the specific rates of all core collapse SNe decrease with increasing stellar mass.
Type Ia supernovae (SNe Ia) are used as standardisable candles to measure cosmological distances, but differences remain in their corrected luminosities which display a magnitude step as a function ...of host galaxy properties such as stellar mass and rest-frame \(U-R\) colour. Identifying the cause of these steps is key to cosmological analyses and provides insight into SN physics. Here we investigate the effects of SN progenitor ages on their light curve properties using a galaxy-based forward model that we compare to the Dark Energy Survey 5-year SN Ia sample. We trace SN Ia progenitors through time and draw their light-curve width parameters from a bimodal distribution according to their age. We find that an intrinsic luminosity difference between SNe of different ages cannot explain the observed trend between step size and SN colour. The data split by stellar mass are better reproduced by following recent work implementing a step in total-to-selective dust extinction ratio \((R_V)\) between low- and high-mass hosts, although an additional intrinsic luminosity step is still required to explain the data split by host galaxy \(U-R\). Modelling the \(R_V\) step as a function of galaxy age provides a better match overall. Additional age vs. luminosity steps marginally improve the match to the data, although most of the step is absorbed by the width vs. luminosity coefficient \(\alpha\). Furthermore, we find no evidence that \(\alpha\) varies with SN age.
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 on state-of-the-art simulations of photometrically identified SN Ia samples and determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-year SN sample. As part of the analysis, we test on our DES simulations the performance of SuperNNova, a photometric SN classifier based on recurrent neural networks. Depending on the choice of non-Ia SN models in both the simulated data sample and training sample, contamination ranges from 0.8-3.5 %, with the efficiency of the classification from 97.7-99.5 %. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension 'BEAMS with Bias Correction' (BBC), we produce a redshift-binned Hubble diagram marginalised over contamination and corrected for selection effects and we use it to constrain the dark energy equation-of-state, \(w\). Assuming a flat universe with Gaussian \(\Omega_M\) prior of \(0.311\pm0.010\), we show that biases on \(w\) are \(<0.008\) when using SuperNNova and accounting for a wide range of non-Ia SN models in the simulations. Systematic uncertainties associated with contamination are estimated to be at most \(\sigma_{w, \mathrm{syst}}=0.004\). This compares to an expected statistical uncertainty of \(\sigma_{w,\mathrm{stat}}=0.039\) for the DES-SN sample, thus showing that contamination is not a limiting uncertainty in our analysis. We also measure biases due to contamination on \(w_0\) and \(w_a\) (assuming a flat universe), and find these to be \(<\)0.009 in \(w_0\) and \(<\)0.108 in \(w_a\), hence 5 to 10 times smaller than the statistical uncertainties expected from the DES-SN sample.
We present the transient source detection efficiencies of the Palomar Transient Factory (PTF), parameterizing the number of transients that PTF found, versus the number of similar transients that ...occurred over the same period in the survey search area but that were missed. PTF was an optical sky survey carried out with the Palomar 48-inch telescope over 2009-2012, observing more than 8000 square degrees of sky with cadences of between 1 and 5 days, locating around 50,000 non-moving transient sources, and spectroscopically confirming around 1900 supernovae. We assess the effectiveness with which PTF detected transient sources, by inserting ~7 million artificial point sources into real PTF data. We then study the efficiency with which the PTF real-time pipeline recovered these sources as a function of the source magnitude, host galaxy surface brightness, and various observing conditions (using proxies for seeing, sky brightness, and transparency). The product of this study is a multi-dimensional recovery efficiency grid appropriate for the range of observing conditions that PTF experienced, and that can then be used for studies of the rates, environments, and luminosity functions of different transient types using detailed Monte Carlo simulations. We illustrate the technique using the observationally well-understood class of type Ia supernovae.
