halomod.bias.Seljak04Cosmo

class halomod.bias.Seljak04Cosmo(nu: numpy.ndarray, delta_c: float = 1.686, m: Optional[numpy.ndarray] = None, mstar: Optional[float] = None, delta_halo: Optional[float] = 200, n: Optional[float] = 1, sigma_8: Optional[float] = 0.8, cosmo: astropy.cosmology.core.FLRW = FlatLambdaCDM(name="Planck15", H0=67.7 km / (Mpc s), Om0=0.307, Tcmb0=2.725 K, Neff=3.05, m_nu=[0.   0.   0.06] eV, Ob0=0.0486), n_eff: Optional[numpy.ndarray] = None, z: float = 0.0, **model_parameters)[source]

Empirical bias relation from Seljak & Warren (2004), with cosmological dependence.

See documentation for Bias for information on input parameters. This model has no free parameters.

Notes

This the form from [1] with cosmological dependence – except we do not include the running of the spectral index. The form is

\[b_{\rm no cosmo} + \log_10(x) \left[a_1 (\Omega_{m,0} - 0.3 + n - 1) + a_2(\sigma_8 - 0.9 + h-0.7)\right]\]

with \(x = m/m_\star\) (and \(m_{\star}\) the nonlinear mass – see Bias for details). The non-cosmologically-dependent bias is that given by Seljak04. a1 and a2 are fitted, with values given in [1] as (a1,a2) = (0.4, 0.3).

Parameters

  • a (float, optional) – The fitted parameters for Seljak04.

  • b (float, optional) – The fitted parameters for Seljak04.

  • c (float, optional) – The fitted parameters for Seljak04.

  • d (float, optional) – The fitted parameters for Seljak04.

  • e (float, optional) – The fitted parameters for Seljak04.

  • f (float, optional) – The fitted parameters for Seljak04.

  • g (float, optional) – The fitted parameters for Seljak04.

  • a1 (float, optional) – Fitted parameters for the cosmological dependence.

  • a2 (float, optional) – Fitted parameters for the cosmological dependence.

References

1(1,2)

Seljak, U. and Warren M. S., “Large-scale bias and stochasticity of haloes and dark matter”, https://ui.adsabs.harvard.edu/abs/2004MNRAS.355..129S, 2004.

Methods

__init__(nu[, delta_c, m, mstar, …])

Initialize self.

bias()

Calculate the first-order, linear, deterministic halo bias.

get_models()

Get a dictionary of all implemented models for this component.

Attributes

pair_hmf

The HMF model that pairs with this bias in the peak-background split