Exploding stars that show no Hydrogen in their spectra are called Type 1 supernovovae. They are vital for many areas of astrophysics. The most famous sub-class are the Type Ia, which have yielded exciting results about the accelerating expansion of the universe. However, for the next step in precision cosmology, identifications are needed of Type Ia at very large distances, which is difficult, because they are easily confused with other types of Type I supernovae, namely Type Ib/c. This survey will study the observed differences between these supernova types to eliminate mistaken classifications and thus enable a better understanding of the acceleration of our universe at earlier stages in its expansion, billions of years in the past.
While Type Ia are the result of thermonuclear explosions of a C-O white dwarf in a binary system, Type Ib/c are core-collapse explosions from massive stars, which have been removed of their outermost H layer. Type Ib/c observations cannot be used to derive accurate distance measurements; however, because their spectra look like Type 1a supernovae, they can contaminate large supernova surveys. This award is to conduct a thorough statistical analysis of optical spectra and lightcurves (records of light emitted over time) of thousands of supernovae in order to distinguish between Type Ia supernova and Types 1b and 1c and remove the impostors from the surveys. This project will develop the tools needed to quickly and automatically identify and remove Type 1b/c supernovae from surveys. The PI will provide these tools and methods to the community for future observations with other telescopes, such as the Large Synoptic Survey Telescope (LSST), which is expected to yield thousands of new supernova candidates per night. The data will also be used to test models of core-collapse explosions of massive stars, which will improve knowledge of stellar evolution. The PI will train and mentor a graduate student and undergraduate student in research, and she will also collaborate with an external organization to recruit a promising high school student from an underrepresented group in the fields of Science, Technology, Engineering and Math (STEM).
The work will be done through a two-pronged approach: 1) utilizing the traditional spectral template approach with well-established algorithms, and then 2) employing Principal Component Analysis (PCA) to derive eigenspectrum bases, which if found in a robust fashion, can be easily used to construct average spectral time-series templates and to identify any new classification schemes within the available data. In addition, the PI will clarify the link between SN spectral classification for SN Ib/c, including those connected with Gamma-Ray Bursts (GRBs), and their purported stellar progenitors by testing for hidden He in SN Ic. This will be done by comparing observed spectra and light curves of a large set of SN Ib/c, for the first time, with those predicted from two competing models. The PI will use spectra and lightcurves of over 5800 supernova explosions that are recently published (such as WISeREP and SUSPECT) or are in-hand.
