halomod.bias.Mandelbaum05¶

class halomod.bias.Mandelbaum05(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

Empirical bias of Mandelbaum (2005).

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

Notes

This form is the same as that of SMT99, however the parameters were re-fit to simulations from [1] in [2]. Here the default parameters are (q,p) = (0.73, 0.15).

References

[1]

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.

[2]

Mandelbaum, R. et al., “Galaxy-galaxy lensing: dissipationless simulations versus the halo model”, https://ui.adsabs.harvard.edu/abs/2005MNRAS.362.1451M, 2005.

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.SMT'>,)¶: The HMF model that pairs with this bias in the peak-background split