Papers › Addressing type Ia supernova color variability with a linear spectral template
Addressing type Ia supernova color variability with a linear spectral template
Cássia S. Nascimento, João Paulo C. França, Ribamar R. R. Reis
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Type Ia Supernovae (SNeIa) provided the first evidence of an accelerated expansion of the universe and remain a valuable probe to cosmology. They are deemed standardizable candles due to the observed correlations between its luminosity and photometric quantities. This characteristic can be exploited to estimate cosmological distances after accounting for the observed variations. There is however a remaining dispersion unaccounted for in the current state-of-the-art standardization methods. In an attempt to explore this issue, we propose a simple linear 3-component rest-frame flux description for a light-curve fitter. Since SNIa intrinsic color index variations are expected to be time-dependent, our description builds-up upon the mathematical expression of the well known SALT2 for rest-frame flux, whilst we drop the exponential factor and add an extra model component with time and wavelength dependencies. The model components are obtained by performing either Principal Component Analysis (PCA) or Factor Analysis (FA) onto a representative training set. The constraining power of the Pure Expansion Template for Supernovae (PETS), is evaluated and we found compatible results with SALT2 for Ωₘ₀ and Ω_(Λ0) within 68% uncertainty between the two models, with PETS' fit parameters exhibiting non negligible linear correlations with SALT2' parameters. For both model versions we verified that the first component describes mainly color index variations, as a dominant effect on SNIa spectra. The model nuisance parameter which multiplies the color index variation-like fit parameter shows evolution with redshift in an initial binned cosmology analysis. This behavior can be due to selection effects. Overall, our model shows promise, as there are still a few aspects to be refined; however, it still falls short in reducing the unaccounted dispersion.
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