Core-collapse supernovae in the dark energy survey

Abstract

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.

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Identifiers

ORCID for Mark Sullivan: orcid.org/0000-0001-9053-4820

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