Plus ultra: extreme supernovae beyond the standard paradigm of cosmic explosions

Supernovae (SNe), stellar explosions staging the final act of a star's life, play an important role in many astrophysical domains, for instance stellar evolution, feedback in galaxy formation, synthesis and distribution of almost all the elements and raw materials for both star and planet formation.
The last ten years, with the advent of wide-field surveys, have opened up a new parameter space in time-domain astronomy with the surprising discovery of transients defying our understanding of how stars explode. These can be grouped into three categories: 1- a population of ultra-bright 'superluminous' supernovae, some 100 times brighter than classical supernova types, offering new probes of the high redshift universe and the potential for a new class of standard candle; 2 - transients showing fast rise and subsequent rapid decay that do not resemble any common class of extragalactic transient; 3 - transients with extreme energetics or complex evolution happening in low-metallicity or low-luminosity environments.

The consensus is that metallicity, initial mass and multiplicity influence the type of SN we observe but their precise role has not been characterised. This impedes our ability to use SN as probes of star formation across the Universe and to understand if the progenitor metallicity is encoded within the mass and explosion mechanism of such extreme supernovae. Detailed analyses of such SNe and their hosts, both in their local (supernova position) and global (host galaxy as a whole) environment, have generally been restricted to a few, usually luminous, host galaxies. Machine learning approaches will then be used to retrieve any link between the environmental information and those retrieved from the supernova. If the dataset will be rich enough, an Artificial intelligence algorithm can be built to predict what kind of galaxy will likely be the host of future extreme Supernovae and to recognize them early enough in the 22 Terabyte stream of data (per observing night) that the Vera Rubin observatory will deliver from 2023.

In this project, the PhD student will gather knowledge of supernova explosions linked to the environment properties as well as programming skills in python, machine learning / Artificial Intelligence, experience in observational astronomy and statistics.