For the past few decades, scientists have been measuring the expansion of the universe. Only recently, they discovered that this expansion is actually accelerating due to dark energy. One useful tool to estimate this acceleration is Type Ia supernovae, standard candles with an intrinsic luminosity that follow a specific brightness curve. However, telescope observations of these supernovae are contaminated by their host galaxies, so it is imperative that we obtain clean supernova spectra by subtracting the host galaxy spectrum from the observed spectrum. This can be done through the simulation of plausible galaxies at various redshifts, using Sloan Digital Sky Survey imaging data to generate model spectra stored in data cubes. 

In order to construct this synthetic data cube, we require a process involving automatic downloads of SDSS data, the resampling, cutting, and cleaning of images, and the generation of noise maps – the goal being to load a file with fluxes and flux errors for each of the five imaging filters into CIGALE. CIGALE takes this discrete input for every galaxy pixel and outputs a continuous model spectrum over a range of wavelengths, which can then be reassembled to form a data cube. 

Results indicate that every pixel within the galaxy has a slightly different model spectrum, which is to be expected. Additionally, spectra for pixels seem reasonable within the SDSS wavelength range but erratic elsewhere, confining the range of reasonable modeling to the input data range. In summary, these data cubes, generated for any user-defined redshift or galaxy of interest, may be used for future Roman observations to extract clean SNe spectra. Though various modifications in methodology may lead to diverse cubes, better modeling and standardization of SNe Ia light curves is essential to more accurately estimating dark energy parameters – thus mapping out with greater precision the detailed expansion history of our universe.