halomod.bias.Tinker05¶

class halomod.bias.Tinker05(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: SMT01

Empirical bias from Tinker et al (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 SMT01, however the parameters were re-fit to simulations in [1]. Here the default parameters are (a,b,c) = (0.707, 0.35, 0.8).

References

[1]

Tinker J. et al., “On the Mass-to-Light Ratio of Large-Scale Structure”, https://ui.adsabs.harvard.edu/abs/2005ApJ…631…41T, 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