Here you will find brief summaries of some of my research projects. To see my full publication list go to ADS.
A Detailed HST Look of Superluminous Supernova Host Galaxies
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Photometrically-Classified Superluminous Supernovae
In this project, we explore how accurately machine learning algorithms can classify Type I superluminous supernovae based solely on their light curves.
Magnetar Models of Superluminous Supernovae from the Dark Energy Survey
Type I superluminous supernovae (SLSNe) are a super rare class of core-collapse supernovae that emit 10-100 times more energy. This means that they can be seen to high redshifts. The 21 SLSNe from the Dark Energy Survey probe some of the highest-redshift SLSN to date. In this project, we investigate their light curve evolution by fitting a magnetar central engine model and compare model parameters against cosmic time.
Brian Hsu 