Best-Fit Bias

Compute systematic bias on parameters from neglecting contributions in the model.

Method

Best-fit bias is computed via get_fish by passing a bias_list argument specifying the neglected term(s). The resulting FisherMat object stores the bias values in its .bias attribute.

fisher = forecast.get_fish(
    param_list,
    terms='NPP',
    pkln=[0, 2],
    bias_list='WS'   # neglected term(s)
)

# Access bias values
fisher.bias

Usage

import cosmo_wap as cw
from cosmo_wap.lib import utils
from cosmo_wap.forecast import FullForecast

cosmo = utils.get_cosmo(h=0.67, Omega_m=0.31)
survey = cw.SurveyParams.Euclid(cosmo)
cosmo_funcs = cw.ClassWAP(cosmo, survey)
forecast = FullForecast(cosmo_funcs, kmax_func=0.15, N_bins=4)

# Bias on fNL from neglecting wide-separation
fisher = forecast.get_fish(
    ["fNL", "A_s", "n_s"],
    terms=["NPP", "Loc"],
    pkln=[0, 2],
    bias_list="WS"
)

for param in ["fNL", "A_s", "n_s"]:
    print(f"Bias on {param}: {fisher.bias[param]:.4e}")

# Bias from neglecting GR effects
fisher_gr = forecast.get_fish(
    ["fNL", "A_s", "n_s"],
    terms=["NPP", "Loc"],
    pkln=[0, 2],
    bias_list="GR1"
)

Alternative: best_fit_bias

Bias can also be computed directly via the standalone method:

FullForecast.best_fit_bias(param, bias_term, terms='NPP', pkln=None, bkln=None, verbose=True, sigma=None)

Compute parameter bias from neglecting a contribution.

Parameters:

• param – Parameter(s) to compute bias for (string or list)

• bias_term – Neglected term(s) (see Available Terms)

• terms (str) – Base terms (see Available Terms, default: 'NPP')

• pkln (list) – Pk multipoles

• bkln (list) – Bk multipoles

• verbose (bool) – Show progress

• sigma (float) – FoG damping

Returns:

Tuple of (bias_dict, fisher_diagonal)

bias, fisher_diag = forecast.best_fit_bias(
    ["fNL", "A_s", "n_s"],
    bias_term="WS",
    terms=["NPP", "Loc"],
    pkln=[0, 2]
)

for param in ["fNL", "A_s", "n_s"]:
    print(f"Bias on {param}: {bias[param]:.4e}")