Extracting high-level information from gamma-ray burst supernova spectra
Radiation transport codes are often used in astrophysics to construct spectral models. In this work we demonstrate how producing these models for a time series of data can provide unique information about supernovae (SNe). Unlike previous work, we specifically concentrate on the method for obtaining the best synthetic spectral fits, and the errors associated with the preferred model parameters. We demonstrate how varying the ejecta mass, bolometric luminosity ($L_{bol}$) and photospheric velocity ($v_{ph}$), affects the outcome of the synthetic spectra. As an example we analyze the photospheric phase spectra of the GRB-SN\,2016jca. It is found that for most epochs (where the afterglow subtraction is small) the error on $L_{bol}$ and $v_{ph}$ was $\sim$5\%. The uncertainty on ejecta mass and K.E. was found to be $\sim$20%, although this can be expected to dramatically decrease if models of nebular phase data can be simultaneously produced. We also demonstrate how varying the elemental abundance in the ejecta can produce better synthetic spectral fits. In the case of SN\,2016jca it is found that a decreasing $^{56}$Ni abundance as a function of decreasing velocity produces the best fit models. This could be the case if the $^{56}$Ni was sythesised at the side of the GRB jet, or dredged up from the centre of the explosion. The work presented here can be used as a guideline for future studies on supernovae which use the same or similar radiation transfer code.