CosmoFit.DESSN5YRLikelihood

class CosmoFit.DESSN5YRLikelihood(cosmology, version='des-sn5yr', marginalize_offset=True)[source]

Bases: BaseLikelihood, AnalyticOffsetMixin

DES-SN5YR (Dark Energy Survey 5-year) Supernova likelihood.

Unlike PantheonLikelihood (which compares an apparent-magnitude-like observable, m_b_corr, to mu(z) + M_B), DES-SN5YR distributes the SN distance modulus directly (MU, computed assuming a fiducial H0=70). Comparing that to a model mu(z) computed at a different H0 leaves a constant offset – the same H0/absolute-calibration degeneracy Pantheon+ has, just expressed directly in distance- modulus space rather than magnitude space – which is marginalized over analytically the same way (see AnalyticOffsetMixin); this exactly matches the DES-SN5YR data release’s own reference likelihood (DES-Dovekie-SN_Likelihood.py, cov_log_likelihood).

Parameters:

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

  • version (str) – DES-SN5YR dataset version.

  • marginalize_offset (bool, optional) – If True (default), the constant distance-modulus offset (fully degenerate with H0) is marginalized over analytically. If False, cosmology.MB is added to the model as an explicit free/fixed nuisance parameter instead (mu_model = mu(z) + M_B).

Warning

Do not combine "pantheon" and "des_sn5yr" in the same Fitter fit. DES-SN5YR’s low-redshift “anchor” sample includes CfA3, CfA4, and Foundation supernovae (~11% of its 1820 SNe, IDSURVEY != 10) that are also compiled into Pantheon+ – fitting both at once double-counts those supernovae (correlated, non-independent data treated as independent), understating uncertainties and biasing any joint result. Use one SN Ia compilation per fit, not both.

References

Sanchez et al. (2024), arXiv:2406.05046 (data release). DES Collaboration (2024), arXiv:2401.02929 (cosmology results).

__init__(cosmology, version='des-sn5yr', marginalize_offset=True)[source]
Parameters:

  • version (str)

  • marginalize_offset (bool)

Methods

__init__(cosmology[, version, ...])

best_fit_offset()

Best-fit additive offset that analytic marginalization would assign, given the current cosmology.

chi2()

Chi-square statistic.

log_likelihood()

Return log-likelihood.

marginalized_chi2()

Chi-square with the offset analytically marginalized out.

model()

Predicted distance modulus.

predictions()

Alias for model().

residuals()

Data minus model residuals.

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.