Model independent analysis of supernova data, dark energy, trans-Planckian censorship and the swampland

In this Letter, we consider the model-independent reconstruction of the expansion and growth functions from the Pantheon supernova data. The method relies on developing the expansion function in terms of shifted Chebyshev polynomials and determining the coefficients of the polynomials by a maximum-likelihood fit to the data. Having obtained the expansion function in a model-independent way, we can then also determine the growth function without assuming a particular model. We then compare the results with the predictions of two classes of Dark Energy models, firstly a class of quintessence scalar field models consistent with the trans-Planckian censorship and swampland conjectures, and secondly a class of generalized Proca vector field models. We determine constraints on the parameters which appear in these models. (C) 2020 The Author(s). Published by Elsevier B.V.

Automated summary

In this Letter, the model-independent reconstruction of the expansion and growth functions from Pantheon supernova data is considered, using shifted Chebyshev polynomials and maximum-likelihood fit. The obtained results show the possibility of determining the growth function without assuming a specific model, and compare with predictions of two classes of Dark Energy models, constraining the parameters in these models.