GausSN: Bayesian Time-Delay Estimation for Strongly Lensed Supernovae

We present GausSN, a Bayesian semi-parametric Gaussian Process (GP) model for time-delay estimation with resolved systems of gravitationally lensed supernovae (glSNe). GausSN models the underlying light curve non-parametrically using a GP. Without assuming a template light curve for each SN type, GausSN fits for the time delays of all images using data in any number of wavelength filters simultaneously. We also introduce a novel time-varying magnification model to capture the effects of microlensing alongside time-delay estimation. In this analysis, we model the time-varying relative magnification as a sigmoid function, as well as a constant for comparison to existing time-delay estimation approaches. We demonstrate that GausSN provides robust time-delay estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope and the Vera C. Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST). We find that up to 43.6% of time-delay estimates from Roman and 52.9% from Rubin-LSST have fractional errors of less than 5%. We then apply GausSN to SN Refsdal and find the time delay for the fifth image is consistent with the original analysis, regardless of microlensing treatment. Therefore, GausSN maintains the level of precision and accuracy achieved by existing time-delay extraction methods with fewer assumptions about the underlying shape of the light curve than template-based approaches, while incorporating microlensing into the statistical error budget rather than requiring post-processing to account for its systematic uncertainty. GausSN is scalable for time-delay cosmography analyses given current projections of glSNe discovery rates from Rubin-LSST and Roman.Comment: 18 pages, 12 figures, submitted to MNRA

The effect of environment on type Ia supernovae in the dark energy survey three-year cosmological sample

Analyses of type Ia supernovae (SNe Ia) have found puzzling correlations between their standardised luminosities and host galaxy properties: SNe Ia in high-mass, passive hosts appear brighter than those in lower-mass, star-forming hosts. We examine the host galaxies of SNe Ia in the Dark Energy Survey three-year spectroscopically-confirmed cosmological sample, obtaining photometry in a series of ‘local’ apertures centred on the SN, and for the global host galaxy. We study the differences in these host galaxy properties, such as stellar mass and rest-frame U − R colours, and their correlations with SN Ia parameters including Hubble residuals. We find all Hubble residual steps to be >3σ in significance, both for splitting at the traditional environmental property sample median and for the step of maximum significance. For stellar mass, we find a maximal local step of 0.098 ± 0.018 mag; ∼0.03 mag greater than the largest global stellar mass step in our sample (0.070 ± 0.017 mag). When splitting at the sample median, differences between local and global U − R steps are small, both ∼0.08 mag, but are more significant than the global stellar mass step (0.057 ± 0.017 mag). We split the data into sub-samples based on SN Ia light curve parameters: stretch (x1) and colour (c), finding that redder objects (c > 0) have larger Hubble residual steps, for both stellar mass and U − R, for both local and global measurements, of ∼0.14 mag. Additionally, the bluer (star-forming) local environments host a more homogeneous SN Ia sample, with local U − R r.m.s. scatter as low as 0.084 ± 0.017 mag for blue (c < 0) SNe Ia in locally blue U − R environments

Core-collapse supernovae in the dark energy survey

Core-collapse supernovae – the deaths of massive stars – are among the most complex and diverse astrophysical phenomena, demonstrating a wide range of spectroscopic and photometric properties. These events result from the cessation of fusion in the cores of massive stars causing gravitational collapse, but there is a great deal that remains uncertain about the exact mechanisms involved. Studying the properties of populations of CCSNe can help constrain our knowledge of the physics involved in the explosion. In this thesis, I examine the properties of high-redshift core-collapse supernova in the Dark Energy Survey (DES) and compare with local samples from the Lick Observatory Supernova Search (LOSS) and Zwicky Transient Facility (ZTF). Comparing type II SNe in DES and ZTF, I see a difference in peak luminosity of 3.0σ significance and between LOSS and ZTF of 2.5σ. This could be caused by redshift evolution, although simpler causes such as differing levels of host galaxy extinction between the samples cannot be ruled out. I also examine host galaxy properties for these samples, finding an offset in host galaxy colour between DES and ZTF; for the same galaxy stellar mass, a DES galaxy is bluer than a ZTF galaxy. I consider a number of simple explanations for 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.During my analysis, I identified a very luminous SN IIb, DES14X2fna. This SN had an unusually high luminosity for its class, reaching ∼ −19.4 mag in r-band, and also declined rapidly after peak. SNe IIb are thought to be powered by the decay of 56Ni, but the mass of Ni that would be required to power this luminosity is inconsistent with the fast decline observed. This suggests that some other physics is involved. Using semi-analytic model fits, I show that 56Ni decay alone is unable to power this object, but interaction with surrounding circumstellar material (CSM) or the spin-down of a rapidly rotating neutron star formed in the explosion are two possible explanations for this unusual object.Finally, I explore the use of Generative Adversarial Networks (GANs) to generate synthetic CCSN light curves. GANs are a type of neural network used for data generation; this approach could be used to augment samples used to train photometric classification algorithms, improving their performance. By training on DES-like simulations I find that GANs are able to generate physically realistic light curves for a variety of CCSN types, demonstrating their potential to improve classification techniques going forward

<scp>Bird-Snack</scp>: Bayesian Inference of dust law <i>RV</i> Distributions using SN Ia Apparent Colours at peaK

Abstract To reduce systematic uncertainties in Type Ia supernova (SN Ia) cosmology, the host galaxy dust law shape parameter, RV, must be accurately constrained. We thus develop a computationally-inexpensive pipeline, Bird-Snack, to rapidly infer dust population distributions from optical-near infrared SN colours at peak brightness, and determine which analysis choices significantly impact the population mean RV inference, $\mu _{R_V}$. Our pipeline uses a 2D Gaussian process to measure peak BVriJH apparent magnitudes from SN light curves, and a hierarchical Bayesian model to simultaneously constrain population distributions of intrinsic and dust components. Fitting a low-to-moderate-reddening sample of 65 low-redshift SNe yields $\mu _{R_V}=2.61^{+0.38}_{-0.35}$, with $68~{{\%}}(95~{{\%}})$ posterior upper bounds on the population dispersion, \sigma _{R_V}&lt;0.92(1.96). This result is robust to various analysis choices, including: the model for intrinsic colour variations, fitting the shape hyperparameter of a gamma dust extinction distribution, and cutting the sample based on the availability of data near peak. However, these choices may be important if statistical uncertainties are reduced. With larger near-future optical and near-infrared SN samples, Bird-Snack can be used to better constrain dust distributions, and investigate potential correlations with host galaxy properties. Bird-Snack is publicly available; the modular infrastructure facilitates rapid exploration of custom analysis choices, and quick fits to simulated datasets, for better interpretation of real-data inferences.</jats:p

Thesis dataset: Core-collapse supernovae in the Dark Energy Survey

This data set contains the data underpinning the thesis &quot;Core-collapse supernovae in the Dark Energy Survey&quot;</span

Keck Infrared Transient Survey Data Release 1

We present the first data release from the Keck Infrared Transient Survey (KITS), a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types. This data release consists of 105 NIR spectra of 50 transients. As we are entering a new era of infrared astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman), KITS provides a large, publicly available sample of IR spectroscopy for a wide range of transients. These data will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. The first data release represents the first semester, which is one third of the full survey. We systematically observed three samples: a flux-limited sample that includes all transients brighter than 17~mag in a red optical band (usually ZTF r or ATLAS o bands); a volume-limited sample including all transients within redshift z < 0.01; and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. Please see the accompanying paper where we describe our observing procedures and data reduction using an automated pipeline pypeit with minimal human interaction to ensure reproducibility. In this dataset, we provide telluric-corrected spectra of the transient in CSV format. We also provide one-dimensional extracted spectra of transients and telluric standard stars in FITS format from pypeit. Users can use these intermediate data products to redo telluric correction if desired

SN2023ixf in Messier 101: the twilight years of the progenitor as seen by Pan-STARRS

The nearby type II supernova, SN2023ixf in M101 exhibits signatures of early-time interaction with circumstellar material in the first week post-explosion. This material may be the consequence of prior mass loss suffered by the progenitor which possibly manifested in the form of a detectable pre-supernova outburst. We present an analysis of the long-baseline pre-explosion photometric data in $g$, $w$, $r$, $i$, $z$ and $y$ filters from Pan-STARRS as part of the Young Supernova Experiment, spanning $\sim$5,000 days. We find no significant detections in the Pan-STARRS pre-explosion light curve. We train a multilayer perceptron neural network to classify pre-supernova outbursts. We find no evidence of eruptive pre-supernova activity to a limiting absolute magnitude of $-7$. The limiting magnitudes from the full set of $gwrizy$ (average absolute magnitude $\approx$-8) data are consistent with previous pre-explosion studies. We use deep photometry from the literature to constrain the progenitor of SN2023ixf, finding that these data are consistent with a dusty red supergiant (RSG) progenitor with luminosity $\log\left(L/L_\odot\right)$$\approx$5.12 and temperature $\approx$3950K, corresponding to a mass of 14-20 M$_\odot

Understanding the extreme luminosity of DES14X2fna

ABSTRACT We present DES14X2fna, a high-luminosity, fast-declining Type IIb supernova (SN IIb) at redshift z = 0.0453, detected by the Dark Energy Survey (DES). DES14X2fna is an unusual member of its class, with a light curve showing a broad, luminous peak reaching Mr ≃ −19.3 mag 20 d after explosion. This object does not show a linear decline tail in the light curve until ≃60 d after explosion, after which it declines very rapidly (4.30 ± 0.10 mag 100 d−1 in the r band). By fitting semi-analytic models to the photometry of DES14X2fna, we find that its light curve cannot be explained by a standard 56Ni decay model as this is unable to fit the peak and fast tail decline observed. Inclusion of either interaction with surrounding circumstellar material or a rapidly-rotating neutron star (magnetar) significantly increases the quality of the model fit. We also investigate the possibility for an object similar to DES14X2fna to act as a contaminant in photometric samples of SNe Ia for cosmology, finding that a similar simulated object is misclassified by a recurrent neural network (RNN)-based photometric classifier as an SN Ia in ∼1.1–2.4 per cent of cases in DES, depending on the probability threshold used for a positive classification