halomod.bias.Pillepich10

class halomod.bias.Pillepich10(nu: ~numpy.ndarray, delta_c: float = 1.686, m: ~numpy.ndarray | None = None, mstar: float | None = None, delta_halo: float | None = 200, n: float | None = 1, sigma_8: float | None = 0.8, cosmo: ~astropy.cosmology.flrw.base.FLRW = FlatLambdaCDM(name='Planck15', H0=<Quantity 67.74 km / (Mpc s)>, Om0=0.3075, Tcmb0=<Quantity 2.7255 K>, Neff=3.046, m_nu=<Quantity [0., 0., 0.06] eV>, Ob0=0.0486), n_eff: None | ~numpy.ndarray = None, z: float = 0.0, **model_parameters)[source]

Bases: Bias

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

bias()[source]

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

Returns:

b – The bias as a function of mass, as an array of values corresponding to the instance attributes m and/or nu.

Return type:

array-like

Examples

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from halomod.bias import Mo96
>>> peak_height = np.linspace(0.1, 2, 100)
>>> bias = Mo96(nu=peak_height)
>>> plt.plot(peak_height, bias.bias())
classmethod get_models() → Dict[str, Type]

Get a dictionary of all implemented models for this component.

pair_hmf = ()

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