This week, Eli Kasai gave us a short talk about the work he is doing for his masters project with NASSP. He is using image subtraction to find type 1a supernovae to learn more about supernovae rates in galaxy clusters.
Knowledge of 1a supernovae rates is important because it can tell us about the physics of supernovae, about iron (and other component) abundance in the intra-cluster medium and it could help improve constraints on cosmological parameters.
Eli has used two image subtraction algorithms to detect supernovae: IRAF, which is a manual image subtraction algorithm and ISIS, which is automated. The image subtraction process basically involves convolving the two images to the worst seeing, normalising the fluxes and then subtracting. Eli found that the ISIS algorithm was considerably faster due to its automation.