CosmoFit.BAOLowZLikelihood

class CosmoFit.BAOLowZLikelihood(cosmology, version='6dfgs+mgs')

Bases: BAODistanceLikelihood

The two BAO measurements below z = 0.2: 6dFGS at z = 0.106 (Beutler et al. 2011) and the SDSS DR7 Main Galaxy Sample at z = 0.15 (Ross et al. 2015).

Every other BAO dataset in this library starts at z = 0.295 (DESI) or z = 0.38 (BOSS DR12), so these two points are the only BAO leverage available in the regime where the expansion history is closest to today’s – exactly where a dark-energy equation of state that evolves has to show up. Two points with ~4-5% errors will not, on their own, move a fit much; what they do is extend the lever arm of the BAO-only distance ladder downward, which matters for the H0 * r_d degeneracy that BAO alone cannot break.

Both surveys are independent of DESI and of SDSS BOSS/eBOSS (6dFGS is a different hemisphere and instrument; the DR7 MGS is a brighter, lower-redshift sample than the BOSS LRGs), so unlike "desi" and "sdss_bao" – which must not be combined – this dataset can be added to either.

Two details are handled rather than papered over:

• 6dFGS reports r_s/D_V, not D_V/r_s. That is kept as its own observable type ("rs_over_DV" in MODEL_MAP) rather than inverted in the data file, because inverting a Gaussian gives something that is neither Gaussian nor centred where the inversion of the mean is.

• 6dFGS’s measurement is calibrated against an Eisenstein & Hu (1998) fitting-formula sound horizon (153.9 Mpc for their fiducial) where a Boltzmann code gives 149.8 Mpc for the same cosmology, so the theory r_d is multiplied by 153.9/149.8 before the comparison. See rs_rescale. At 2.7% on a 4.5%-precision point this is not optional.

Parameters:

• cosmology – Cosmology model instance (LCDM, CPL, …).

• version (str, optional) – Dataset version.

References

Beutler et al. (2011), MNRAS 416, 3017, arXiv:1106.3366. Ross et al. (2015), MNRAS 449, 835, arXiv:1409.3242.

__init__(cosmology, version='6dfgs+mgs')

Parameters:

version (str)

Methods

__init__(cosmology[, version])
chi2()	Compute the BAO chi-square.
log_likelihood()	Return log-likelihood.
model()	Compute the theoretical BAO observables.
predictions()	Alias for model().
residuals()	Compute the BAO residual vector.
summary()	Return a summary of the likelihood evaluation.

Attributes

n_data	Number of data points.
name_and_size	Name together with the number of data points.
observable