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In recent years, wide-field sky surveys providing deep multi-band imaging have presented a new path for indirectly characterizing the progenitor populations of core-collapse supernovae (SN): systematic light curve studies. We assemble a set…

In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a…

Core-collapse supernovae (SNe) expand into a medium created by winds from the pre-SN progenitor. The SN explosion and resulting shock wave(s) heat up the surrounding plasma, giving rise to thermal X-ray emission, which depends on the…

The Pan-STARRS (PS1) Medium Deep Survey discovered over 5,000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3,147 of these likely SNe and…

Recent observations of luminous Type IIn supernovae (SNe) provide compelling evidence that massive circumstellar shells surround their progenitors. In this paper we investigate how the properties of such shells influence the SN lightcurve…

Detailed observations of a growing number of supernovae have determined a bloom of new peculiar events. In this paper we take a short tour through the SN diversity and discuss some important, physical issues related to it. Because of the…

In the last decade there has been a remarkable increase in our knowledge about core-collapse supernovae (CC-SNe), and the birthplace of neutron stars, from both the observational and the theoretical point of view. Since the 1930's, with the…

The structure and morphology of supernova remnants (SNRs) reflect the properties of the parent supernovae (SNe) and the characteristics of the inhomogeneous environments through which the remnants expand. Linking the morphology of SNRs to…

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…

We present observations and analysis of 18 stripped-envelope supernovae observed during 2013 -- 2018. This sample consists of 5 H/He-rich SNe, 6 H-poor/He-rich SNe, 3 narrow lined SNe Ic and 4 broad lined SNe Ic. The peak luminosity and…

Determination of the explosion type of supernova remnants (SNRs) can be challenging, as SNRs are hundreds to thousands of years old and supernovae (SNe) are classified based on spectral properties days after explosion. Previous studies of…

The detailed study of supernovae (SNe) and their progenitors allows to better understand the evolution of massive stars and how these end their lives. Despite its importance, the range of physical parameters for the most common type of…

The properties of the population of compact objects created in core-collapse supernovae (SNe) are uncertain. X-ray observations years to decades after the explosions offer a way to gain insight into this, as hard X-ray emission from the…

During the first few hundred days after the explosion, core-collapse supernovae (SNe) emit down-scattered X-rays and gamma-rays originating from radioactive line emissions, primarily from the $^{56}$Ni $\rightarrow$ $^{56}$Co $\rightarrow$…

A fundamental property determining the transient behaviour of core-collapse supernovae (CCSNe) is the amount of radioactive $^{56}$Ni synthesised in the explosion. Using established methods, this is a relatively easy parameter to extract…

We use integrated colors and B and V absolute magnitudes of Type Ia supernova (SN) host galaxies in order to search for environmental effects on the SN optical properties. With the new sample of 44 SNe we confirm the conclusion by Hamuy et…

We improve estimates of stellar mass and mass-weighted average age of Type Ia supernova (SN Ia) host galaxies by combining UV and near-IR photometry with optical photometry in our analysis. Using 206 SNe Ia drawn from the full three-year…

We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is trained on photometric information only, eliminating the need for…

In the era of large all-sky surveys, there will be a need for rapid, automatic classifications of newly discovered transient objects. Our focus here is the classification of supernovae (SNe). We consider random forest machine learning…

We present the cosmological analysis of 752 photometrically-classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host-galaxy spectroscopy from the…