halomod.bias.Manera10¶

class halomod.bias.Manera10(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: ST99

Peak-background split bias from Manera et al. (2010) [1].

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

Parameters:

q (float, optional) – The fitted parameters.
p (float, optional) – The fitted parameters.

Notes

Note

This form from [1] has the same form as ST99, but has refitted the parameters with (q, p) = (0.709, 0.2).

References

[1] (1,2)

Manera M., Sheth,R. K. and Scoccimarro R., “Large-scale bias and the inaccuracy of the peak-background split “, https://ui.adsabs.harvard.edu/abs/2010MNRAS.402..589M, 2010

bias()¶

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 = (<class 'hmf.mass_function.fitting_functions.Manera'>,)¶: The HMF model that pairs with this bias in the peak-background split