halomod.bias.Pillepich10

class halomod.bias.Pillepich10(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)

Empirical bias of Pillepich et al (2010).

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

Notes

This is the fit from [1], but it is the Gaussian case. The form is

\[B_0 + B_1 \sqrt{\nu} + B_2 \nu\]

with \(\nu\) the peak-height parameter. The values of the parameters fitted to simulation are given as (B0, B1, B2) = (0.647, -0.32, 0.568). They are left free to the user.

Parameters

• B1 (float, optional) – The fitted parameters.

• B2 (float, optional) – The fitted parameters.

• B3 (float, optional) – The fitted parameters.

References

1

Pillepich, A., Porciani, C. and Hahn, O., “Halo mass function and scale-dependent bias from N-body simulations with non-Gaussian initial conditions”, https://ui.adsabs.harvard.edu/abs/2010MNRAS.402..191P, 2010

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