Figure 9.
Error ellipses for the BAO parameters α and ϵ from the mocks (grey), the non-Gaussian (fit) models (blue), and noisy realizations of those models (dashed). The discrepancies between the mocks and the Gaussian (fit) model are reduced, though a 3 per cent discrepancy persists in σϵ. The similarity between the two error ellipses indicates that choice of likelihood ($\mathcal {L}_{1}$ or $\mathcal {L}_{2}$) for fitting the non-Gaussian model has very little impact on BAO error estimation.

Error ellipses for the BAO parameters α and ϵ from the mocks (grey), the non-Gaussian (fit) models (blue), and noisy realizations of those models (dashed). The discrepancies between the mocks and the Gaussian (fit) model are reduced, though a 3 per cent discrepancy persists in σϵ. The similarity between the two error ellipses indicates that choice of likelihood (⁠|$\mathcal {L}_{1}$| or |$\mathcal {L}_{2}$|⁠) for fitting the non-Gaussian model has very little impact on BAO error estimation.

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