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MG_forecast.py
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MG_forecast.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import matplotlib.pyplot as plt
from astropy.cosmology import Planck15 as cosmo
from scipy.stats import chi2, norm
import scipy.special as spec
from classy import Class
from scipy.optimize import curve_fit
from bryan_im_utils import *
# In[2]:
from bryan_fisher_tools import *
# In[3]:
# from MG_IM_models_no_atmosphere_with_RSD import *
from MG_IM_models import *
print('imports complete')
# In[4]:
k_max = 5e-2
k_min = -3
bins = 20
# nu_rest = 231
# B = 50
# z_in = 0.5
# D = 10.0 #m Dish size
# theta_b = 1.2*(2.998e8 / ((nu_rest/(1+z_in))*1e9)) # FWHM in radians
# sigma_b = theta_b/2.35 #Gaussian sigma for beam, radians
# field_length = 128 # deg # sky fraction from Chili, 40% --> sqrt(0.4*4 pi Sr * (180/pi)**2) --> Sr --> deg
# field_length = 45
delta_nu = 0.5 #500 Mhz bins
Pn = 480
alpha = -2.8 #-5/3
Beta = -5/3
l_knee = 200 #72.9
eta_min = 0.05
k_List = np.logspace(-5, k_max, 3000)
kList = np.logspace(-5, k_max, 3000) #fix this
z_10_150 = (115. / 150.0) - 1.0 # z < 0
z_21_150 = (230. / 150.0) - 1.0
z_32_150 = (345. / 150.0) - 1.0
z_43_150 = (461. / 150.0) - 1.0
z_54_150 = (576. / 150.0) - 1.0
z_65_150 = (691. / 150.0) - 1.0
z_76_150 = (806. / 150.0) - 1.0
z_98_150 = (1036. / 150.0) - 1.0
nu_10 = 115
nu_21 = 230
nu_32 = 345
nu_43 = 461
nu_54 = 576
nu_65 = 691
nu_76 = 806
nu_98 = 1036
# b_21_150 = 0.2238
# b_32_150 = 0.5008
# b_43_150 = 0.2128
# b_54_150 = 0.0963388
# b_65_150 = 0.04094
# b_76_150 = 0.01634
# b_98_150 = 0.0036565
# b_21_150 = 0.2238
# b_32_150 = 0.5008
# b_43_150 = 0.2128
# b_54_150 = 0.0963388
# b_65_150 = 0.04094
# b_76_150 = 0.01634
# b_98_150 = 0.0036565
z_10_90 = (115. / 95.0) - 1.0
z_21_90 = (230. / 95.0) - 1.0
z_32_90 = (345. / 95.0) - 1.0
z_43_90 = (461. / 95.0) - 1.0
z_54_90 = (576. / 95.0) - 1.0
z_65_90 = (691. / 95.0) - 1.0
z_76_90 = (806. / 95.0) - 1.0
#z_98_90 = (1036. / 95.0) - 1.0
# b_10_90 = 0.098
# b_21_90 = 0.71769
# b_32_90 = 0.617
# b_43_90 = 0.12647
# b_54_90 = 0.04179
# b_65_90 = 0.01566
# b_76_90 = 0.006
z_10_245 = (115. / 245.0) - 1.0
z_21_245 = (230. / 245.0) - 1.0
z_32_245 = (345. / 245.0) - 1.0
z_43_245 = (461. / 245.0) - 1.0
z_54_245 = (576. / 245.0) - 1.0
z_65_245 = (691. / 245.0) - 1.0
z_76_245 = (806. / 245.0) - 1.0
z_98_245 = (1036. / 245.0) - 1.0
#b21_245 =
# b_32_245 = 0.1326
# b_43_245 = 0.0993
# b_54_245 = 0.08868
# b_65_245 = 0.068089
# b_76_245 = 0.0358
# b_98_245 = 0.01044
# updated band-averaged values.
b_10_150, b_10_245, b_10_90 = [0, 0, 0.14125974512653172]
b_21_150, b_21_245, b_21_90 = [0.23814300654524978, 0.05993319334046567, 0.7518435116225263]
b_32_150, b_32_245, b_32_90 = [0.4592268867263453, 0.14029340028678336, 0.6030114302796646]
b_43_150, b_43_245, b_43_90 = [0.206084638401821, 0.0945815471506361, 0.0960524943669473]
b_54_150, b_54_245, b_54_90 = [0.0982545516195774, 0.085691926555859, 0.0343092085605491]
b_65_150, b_65_245, b_65_90 = [0.03998000363105247, 0.06354885042666927, 0.01271079551252695]
b_76_150, b_76_245, b_76_90 = [0.015828137953894098, 0.0346042360296219, 0.00454225728321831]
b_87_150, b_87_245, b_87_90 = [0.00698181582124012, 0.018496707188195648, 0]
b_98_150, b_98_245, b_98_90 = [0.0037430074500111703, 0.01025706724259277, 0]
# shot noise values
I10 = 2.65 #498
I21 = 8.54 # 498
I32_04 = 2.97 #226
I32_13 = 81.7
I32_14 = 100
I32_26 = 295
I43 = 91
I54 = 35
CII = 21
I65 = 9
# In[5]:
def f_Pk_DE(k_eff, Survey, model, *args):
"""
Observable function to pass to the fisher matrix methods. Returns power spectra assuming the propto DE model.
Parameters:
Survey (survey object): object holding survey definition attributes.
model (Modified gravity model object): object holding modified gravity model definition.
args: values of modified gravity model slope parameters in {x_k, x_b, x_m, x_t} order. Only x_b, x_m will be used.
Returns:
numpy array: holding the binned power spectra multiplied by bI^2
"""
x_k = 1
x_b = list(args[0])[0] #have been using 0.6, should make sure all are rerun to 0.63 standard
x_m = list(args[0])[1]
x_t = 0
M = 1
bI21 = (1+Survey.z)*Survey.I
bI21 = bI21**2
MG_model = Modified_Gravity_Model()
MG_model.set_model('DE', x_k, x_b, x_m, x_t, M)
Pks = MG_model.compute_MG_Pk(Survey.z, kList)
MG_expvals, k_eff = get_expvals(Survey.k_bin, Survey.dlogk, Survey.kList, Pks, bintype = 'log')
return bI21 * MG_expvals
def f_Pk_a(k_eff, Survey, model, *args):
"""
Observable function to pass to the fisher matrix methods. Returns power spectra assuming the propto a model.
Parameters:
Survey (survey object): object holding survey definition attributes.
model (Modified gravity model object): object holding modified gravity model definition.
args: values of modified gravity model slope parameters in {x_k, x_b, x_m, x_t} order. Only x_b, x_m will be used.
Returns:
numpy array: holding the binned power spectra multiplied by bI^2
"""
x_k = 1
x_b = list(args[0])[0] #have been using 0.6, should make sure all are rerun to 0.63 standard
x_m = list(args[0])[1]
x_t = 0
M = 1
bI21 = (1+Survey.z)*Survey.I
bI21 = bI21**2
MG_model = Modified_Gravity_Model()
MG_model.set_model('a', x_k, x_b, x_m, x_t, M)
Pks = MG_model.compute_MG_Pk(Survey.z, kList)
MG_expvals, k_eff = get_expvals(Survey.k_bin, Survey.dlogk, Survey.kList, Pks, bintype = 'log')
return bI21 * MG_expvals
def f_Pk_RSD_a_mono(k_eff, Survey, model, *args):
"""
Observable function to pass to the fisher matrix methods. Returns RSD monopole assuming the propto a model.
Parameters:
Survey (survey object): object holding survey definition attributes.
model (Modified gravity model object): object holding modified gravity model definition.
args: values of modified gravity model slope parameters in {x_k, x_b, x_m, x_t} order. Only x_b, x_m will be used.
Returns:
numpy array: holding the binned monopole.
"""
b = (1+Survey.z)
bI = (1+Survey.z)*Survey.I
bI2 = bI*bI
x_k = 1
x_b = list(args[0])[0] #have been using 0.6, should make sure all are rerun to 0.63 standard
x_m = list(args[0])[1]
x_t = 0
M = 1
bI21 = (1+Survey.z)*Survey.I
bI21 = bI21**2
MG_model = Modified_Gravity_Model()
MG_model.set_model('a', x_k, x_b, x_m, x_t, M)
Pks = MG_model.compute_MG_Pk(Survey.z, kList)
fsigma8 = MG_model.compute_fsigma8(Survey.z)
Beta = MG_model.f/b
MG_expvals, k_eff = get_expvals(Survey.k_bin, Survey.dlogk, Survey.kList, Pks, bintype = 'log')
# fix this - needs to return *fsigma8
return (1 + 2/3*Beta + 1/5*Beta**2)*bI2*MG_expvals #update with fsigma8
def f_Pk_RSD_a_quad(k_eff, Survey, model, *args):
"""
Observable function to pass to the fisher matrix methods. Returns RSD quadrupole assuming the propto a model.
Parameters:
Survey (survey object): object holding survey definition attributes.
model (Modified gravity model object): object holding modified gravity model definition.
args: values of modified gravity model slope parameters in {x_k, x_b, x_m, x_t} order. Only x_b, x_m will be used.
Returns:
numpy array: holding the binned RSD quadrupole.
"""
b = (1+Survey.z)
bI = (1+Survey.z)*Survey.I
bI2 = bI*bI
x_k = 1
x_b = list(args[0])[0] #have been using 0.6, should make sure all are rerun to 0.63 standard
x_m = list(args[0])[1]
x_t = 0
M = 1
bI21 = (1+Survey.z)*Survey.I
bI21 = bI21**2
MG_model = Modified_Gravity_Model()
MG_model.set_model('a', x_k, x_b, x_m, x_t, M)
Pks = MG_model.compute_MG_Pk(Survey.z, kList)
fsigma8 = MG_model.compute_fsigma8(Survey.z)
Beta = MG_model.f/b
MG_expvals, k_eff = get_expvals(Survey.k_bin, Survey.dlogk, Survey.kList, Pks, bintype = 'log')
# fix this - needs to return *fsigma8
return (4/3*Beta + 4/7*Beta**2)*bI2*MG_expvals #update with fsigma8
def f_Pk_RSD_DE_mono(k_eff, Survey, model, *args):
"""
Observable function to pass to the fisher matrix methods. Returns RSD quadrupole assuming the propto DE model.
Parameters:
Survey (survey object): object holding survey definition attributes.
model (Modified gravity model object): object holding modified gravity model definition.
args: values of modified gravity model slope parameters in {x_k, x_b, x_m, x_t} order. Only x_b, x_m will be used.
Returns:
numpy array: holding the binned RSD monopole.
"""
b = (1+Survey.z)
bI = (1+Survey.z)*Survey.I
bI2 = bI*bI
x_k = 1
x_b = list(args[0])[0] #have been using 0.6, should make sure all are rerun to 0.63 standard
x_m = list(args[0])[1]
x_t = 0
M = 1
bI21 = (1+Survey.z)*Survey.I
bI21 = bI21**2
MG_model = Modified_Gravity_Model()
MG_model.set_model('DE', x_k, x_b, x_m, x_t, M)
Pks = MG_model.compute_MG_Pk(Survey.z, kList)
fsigma8 = MG_model.compute_fsigma8(Survey.z)
Beta = MG_model.f/b
MG_expvals, k_eff = get_expvals(Survey.k_bin, Survey.dlogk, Survey.kList, Pks, bintype = 'log')
# fix this - needs to return *fsigma8
return (1 + 2/3*Beta + 1/5*Beta**2)*bI2*MG_expvals #update with fsigma8
def f_Pk_RSD_DE_quad(k_eff, Survey, model, *args):
"""
Observable function to pass to the fisher matrix methods. Returns RSD quadrupole assuming the propto DE model.
Parameters:
Survey (survey object): object holding survey definition attributes.
model (Modified gravity model object): object holding modified gravity model definition.
args: values of modified gravity model slope parameters in {x_k, x_b, x_m, x_t} order. Only x_b, x_m will be used.
Returns:
numpy array: holding the binned RSD quadrupole.
"""
b = (1+Survey.z)
bI = (1+Survey.z)*Survey.I
bI2 = bI*bI
x_k = 1
x_b = list(args[0])[0] #have been using 0.6, should make sure all are rerun to 0.63 standard
x_m = list(args[0])[1]
x_t = 0
M = 1
bI21 = (1+Survey.z)*Survey.I
bI21 = bI21**2
MG_model = Modified_Gravity_Model()
MG_model.set_model('DE', x_k, x_b, x_m, x_t, M)
Pks = MG_model.compute_MG_Pk(Survey.z, kList)
fsigma8 = MG_model.compute_fsigma8(Survey.z)
Beta = MG_model.f/b
MG_expvals, k_eff = get_expvals(Survey.k_bin, Survey.dlogk, Survey.kList, Pks, bintype = 'log')
# fix this - needs to return *fsigma8
return (4/3*Beta + 4/7*Beta**2)*bI2*MG_expvals #update with fsigma8
# In[6]:
def run_forecast_mono(Pn, P_shot, B_tar, z_tar, nu_rest_tar, b_in_list, z_in_list, line_intensity_tar, res, band_name, out_file, model_selection, field_length):
"""
Method to define/update parameters of survey and noise models, then run the forecast code and return the result. This method runs the RSD monopole model. Probably redundant.
Parameters:
Pn (float) : "White" (additive) noise level - units are set by the units of bI^2 and the detector time conversion.
B_tar (float) = Target Bandwidth - set by the presence of strong atmospheric emission lines and detector physics
z_tar (float) = Target redshift
nu_rest_tar (float) = Target emission line restframe frequency
b_in_list (array/list) = interloper emission line intensities
z_in_list (array/list) = interloper emission line redshifts
line_intensity_tar (float) = target emission line intensity - units should match/set those of Pn
band_name (str) = bandwidth name for appending to out_file
out_file (str) = out filename
model_selection (str) = Modified gravity evolution parameter choice, either 'a' or 'DE' for propto scale factor and dark energy density, respectively
Returns:
numpy array: Holding fisher matrix for input parameters.
"""
B = B_tar
z_target = z_tar
z_in = z_tar
nu_rest = nu_rest_tar
D = 10.0 #m Dish size
theta_b = 1.2*(2.998e8 / ((nu_rest/(1+z_in))*1e9)) # FWHM in radians
sigma_b = theta_b/2.35 #Gaussian sigma for beam, radians
b_list = b_in_list
z_list = z_in_list
print('resolution is: ')
print(res)
Survey1 = Survey(B, D, theta_b, field_length, res, k_List)
Survey1.set_targets(z_target, nu_rest, line_intensity_tar, k_min, k_max, bins, b_list, z_list)
Survey1.set_survey_Volume()
print(Survey1.V_s)
kList = Survey1.kList
k_bin = Survey1.k_bin
target_line_intensity = line_intensity_tar
MG_model = Modified_Gravity_Model()
if model_selection == 'DE':
MG_model.set_model('DE', 1, 0.63, 0.2, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('RSD_mono', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_RSD_DE_mono, Survey1, 'DE', noises.k_eff, 0.01, noises_test, 0.63, 0.2)
elif model_selection == 'a':
MG_model.set_model('a', 1, 0.48, 0.27, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('RSD_mono', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_RSD_a_mono, Survey1, 'a', noises.k_eff, 0.01, noises_test, 0.48, 0.27)
else:
raise ValueError('Modified Gravity Model must be a or DE.')
return proptoDE_fisher
def run_forecast_quad(Pn, P_shot, B_tar, z_tar, nu_rest_tar, b_in_list, z_in_list, line_intensity_tar, res, band_name, out_file, model_selection, field_length):
"""
Method to define/update parameters of survey and noise models, then run the forecast code and return the result. This method runs the quadrupole error model. Probably redundant.
Parameters:
Pn (float) : "White" (additive) noise level - units are set by the units of bI^2 and the detector time conversion.
B_tar (float) = Target Bandwidth - set by the presence of strong atmospheric emission lines and detector physics
z_tar (float) = Target redshift
nu_rest_tar (float) = Target emission line restframe frequency
b_in_list (array/list) = interloper emission line intensities
z_in_list (array/list) = interloper emission line redshifts
line_intensity_tar (float) = target emission line intensity - units should match/set those of Pn
band_name (str) = bandwidth name for appending to out_file
out_file (str) = out filename
model_selection (str) = Modified gravity evolution parameter choice, either 'a' or 'DE' for propto scale factor and dark energy density, respectively
Returns:
numpy array: Holding fisher matrix for input parameters.
"""
B = B_tar
z_target = z_tar
z_in = z_tar
nu_rest = nu_rest_tar
D = 10.0 #m Dish size
theta_b = 1.2*(2.998e8 / ((nu_rest/(1+z_in))*1e9)) # FWHM in radians
sigma_b = theta_b/2.35 #Gaussian sigma for beam, radians
b_list = b_in_list
z_list = z_in_list
print('resolution is: ')
print(res)
Survey1 = Survey(B, D, theta_b, field_length, res, k_List)
Survey1.set_targets(z_target, nu_rest, line_intensity_tar, k_min, k_max, bins, b_list, z_list)
Survey1.set_survey_Volume()
print(Survey1.V_s)
kList = Survey1.kList
k_bin = Survey1.k_bin
target_line_intensity = line_intensity_tar
MG_model = Modified_Gravity_Model()
if model_selection == 'DE':
MG_model.set_model('DE', 1, 0.63, 0.2, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('RSD_quad', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_RSD_DE_quad, Survey1, 'DE', noises.k_eff, 0.01, noises_test, 0.63, 0.2)
elif model_selection == 'a':
MG_model.set_model('a', 1, 0.48, 0.27, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('RSD_quad', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_RSD_a_quad, Survey1, 'a', noises.k_eff, 0.01, noises_test, 0.48, 0.27)
else:
raise ValueError('Modified Gravity Model must be a or DE.')
return proptoDE_fisher
def run_forecast_mono_interloper(Pn, P_shot, B_tar, z_tar, nu_rest_tar, b_in_list, z_in_list, line_intensity_tar, res, band_name, out_file, model_selection, field_length):
B = B_tar
z_target = z_tar
z_in = z_tar
nu_rest = nu_rest_tar
D = 10.0 #m Dish size
theta_b = 1.2*(2.998e8 / ((nu_rest/(1+z_in))*1e9)) # FWHM in radians
sigma_b = theta_b/2.35 #Gaussian sigma for beam, radians
b_list = b_in_list
z_list = z_in_list
Survey1 = Survey(B, D, theta_b, field_length, res, k_List)
Survey1.set_targets(z_target, nu_rest, line_intensity_tar, k_min, k_max, bins, b_list, z_list)
Survey1.set_survey_Volume()
print(Survey1.V_s)
kList = Survey1.kList
k_bin = Survey1.k_bin
target_line_intensity = line_intensity_tar
MG_model = Modified_Gravity_Model()
if model_selection == 'DE':
MG_model.set_model('DE', 1, 0.63, 0.2, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('RSD_mono_interloper', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_RSD_DE_mono, Survey1, 'DE', noises.k_eff, 0.01, noises_test, 0.63, 0.2)
elif model_selection == 'a':
MG_model.set_model('a', 1, 0.48, 0.27, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('RSD_mono_interloper', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_RSD_a_mono, Survey1, 'a', noises.k_eff, 0.01, noises_test, 0.48, 0.27)
else:
raise ValueError('Modified Gravity Model must be a or DE.')
return proptoDE_fisher
def run_forecast_quad_interloper(Pn, P_shot, B_tar, z_tar, nu_rest_tar, b_in_list, z_in_list, line_intensity_tar, res, band_name, out_file, model_selection, field_length):
B = B_tar
z_target = z_tar
z_in = z_tar
nu_rest = nu_rest_tar
D = 10.0 #m Dish size
theta_b = 1.2*(2.998e8 / ((nu_rest/(1+z_in))*1e9)) # FWHM in radians
sigma_b = theta_b/2.35 #Gaussian sigma for beam, radians
b_list = b_in_list
z_list = z_in_list
Survey1 = Survey(B, D, theta_b, field_length, res, k_List)
Survey1.set_targets(z_target, nu_rest, line_intensity_tar, k_min, k_max, bins, b_list, z_list)
Survey1.set_survey_Volume()
print(Survey1.V_s)
kList = Survey1.kList
k_bin = Survey1.k_bin
target_line_intensity = line_intensity_tar
MG_model = Modified_Gravity_Model()
if model_selection == 'DE':
MG_model.set_model('DE', 1, 0.63, 0.2, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('RSD_quad_interloper', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_RSD_DE_quad, Survey1, 'DE', noises.k_eff, 0.01, noises_test, 0.63, 0.2)
elif model_selection == 'a':
MG_model.set_model('a', 1, 0.48, 0.27, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('RSD_quad_interloper', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_RSD_a_quad, Survey1, 'a', noises.k_eff, 0.01, noises_test, 0.48, 0.27)
else:
raise ValueError('Modified Gravity Model must be a or DE.')
return proptoDE_fisher
def run_forecast_fiducial_mitigated(Pn, P_shot, B_tar, z_tar, nu_rest_tar, b_in_list, z_in_list, line_intensity_tar, res, band_name, out_file, model_selection, field_length):
B = B_tar
z_target = z_tar
z_in = z_tar
nu_rest = nu_rest_tar
D = 10.0 #m Dish size
theta_b = 1.2*(2.998e8 / ((nu_rest/(1+z_in))*1e9)) # FWHM in radians
sigma_b = theta_b/2.35 #Gaussian sigma for beam, radians
b_list = b_in_list
z_list = z_in_list
Survey1 = Survey(B, D, theta_b, field_length, res, k_List)
Survey1.set_targets(z_target, nu_rest, line_intensity_tar, k_min, k_max, bins, b_list, z_list)
Survey1.set_survey_Volume()
print(Survey1.V_s)
kList = Survey1.kList
k_bin = Survey1.k_bin
target_line_intensity = line_intensity_tar
MG_model = Modified_Gravity_Model()
if model_selection == 'DE':
MG_model.set_model('DE', 1, 0.63, 0.2, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('fiducial_mitigated', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_DE, Survey1, 'DE', noises.k_eff, 0.01, noises_test, 0.63, 0.2)
elif model_selection == 'a':
MG_model.set_model('a', 1, 0.48, 0.27, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('fiducial_mitigated', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_a, Survey1, 'a', noises.k_eff, 0.01, noises_test, 0.48, 0.27)
else:
raise ValueError('Modified Gravity Model must be a or DE.')
return proptoDE_fisher
def run_forecast_fiducial_unmitigated(Pn, P_shot, B_tar, z_tar, nu_rest_tar, b_in_list, z_in_list, line_intensity_tar, res, band_name, out_file, model_selection, field_length):
B = B_tar
z_target = z_tar
z_in = z_tar
nu_rest = nu_rest_tar
D = 10.0 #m Dish size
theta_b = 1.2*(2.998e8 / ((nu_rest/(1+z_in))*1e9)) # FWHM in radians
sigma_b = theta_b/2.35 #Gaussian sigma for beam, radians
b_list = b_in_list
z_list = z_in_list
Survey1 = Survey(B, D, theta_b, field_length, res, k_List)
Survey1.set_targets(z_target, nu_rest, line_intensity_tar, k_min, k_max, bins, b_list, z_list)
Survey1.set_survey_Volume()
print(Survey1.V_s)
kList = Survey1.kList
k_bin = Survey1.k_bin
target_line_intensity = line_intensity_tar
MG_model = Modified_Gravity_Model()
if model_selection == 'DE':
MG_model.set_model('DE', 1, 0.63, 0.2, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('fiducial_unmitigated', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_DE, Survey1, 'DE', noises.k_eff, 0.01, noises_test, 0.63, 0.2)
elif model_selection == 'a':
MG_model.set_model('a', 1, 0.48, 0.27, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('fiducial_unmitigated', Survey1, MG_model)
noises_test = noises.sigma
proptoDE_fisher = fisher(f_Pk_a, Survey1, 'a', noises.k_eff, 0.01, noises_test, 0.48, 0.27)
else:
raise ValueError('Modified Gravity Model must be a or DE.')
return proptoDE_fisher
def run_forecast_covariance(Pn, P_shot, B_tar, z_tar, nu_rest_tar, b_in_list, z_in_list, line_intensity_tar, res, band_name, out_file, model_selection, field_length):
B = B_tar
z_target = z_tar
z_in = z_tar
nu_rest = nu_rest_tar
D = 10.0 #m Dish size
theta_b = 1.2*(2.998e8 / ((nu_rest/(1+z_in))*1e9)) # FWHM in radians
sigma_b = theta_b/2.35 #Gaussian sigma for beam, radians
b_list = b_in_list
z_list = z_in_list
Survey1 = Survey(B, D, theta_b, field_length, res, k_List)
Survey1.set_targets(z_target, nu_rest, line_intensity_tar, k_min, k_max, bins, b_list, z_list)
Survey1.set_survey_Volume()
print(Survey1.V_s)
kList = Survey1.kList
k_bin = Survey1.k_bin
target_line_intensity = line_intensity_tar
MG_model = Modified_Gravity_Model()
if model_selection == 'DE':
MG_model.set_model('DE', 1, 0.63, 0.2, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('covariance', Survey1, MG_model)
noise_C00 = noises.sigma[0]
noise_C22 = noises.sigma[1]
noise_C20 = noises.sigma[2]
proptoDE_fisher = fisher_cov(f_Pk_RSD_DE_mono, f_Pk_RSD_DE_quad, Survey1, 'DE', noises.k_eff, 0.01, noise_C00, noise_C22, noise_C20, 0.63, 0.2)
elif model_selection == 'a':
MG_model.set_model('a', 1, 0.48, 0.27, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('covariance', Survey1, MG_model)
noise_C00 = noises.sigma[0]
noise_C22 = noises.sigma[1]
noise_C20 = noises.sigma[2]
proptoDE_fisher = fisher_cov(f_Pk_RSD_a_mono, f_Pk_RSD_a_quad, Survey1, 'a', noises.k_eff, 0.01, noise_C00, noise_C22, noise_C20, 0.48, 0.27)
else:
raise ValueError('Modified Gravity Model must be a or DE.')
return proptoDE_fisher
def run_forecast_covariance_full(Pn, P_shot, B_tar, z_tar, nu_rest_tar, b_in_list, z_in_list, line_intensity_tar, res, band_name, out_file, model_selection, field_length):
B = B_tar
z_target = z_tar
z_in = z_tar
nu_rest = nu_rest_tar
D = 10.0 #m Dish size
theta_b = 1.2*(2.998e8 / ((nu_rest/(1+z_in))*1e9)) # FWHM in radians
sigma_b = theta_b/2.35 #Gaussian sigma for beam, radians
b_list = b_in_list
z_list = z_in_list
Survey1 = Survey(B, D, theta_b, field_length, res, k_List)
Survey1.set_targets(z_target, nu_rest, line_intensity_tar, k_min, k_max, bins, b_list, z_list)
Survey1.set_survey_Volume()
print(Survey1.V_s)
kList = Survey1.kList
k_bin = Survey1.k_bin
target_line_intensity = line_intensity_tar
MG_model = Modified_Gravity_Model()
if model_selection == 'DE':
MG_model.set_model('DE', 1, 0.63, 0.2, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('covariance_full', Survey1, MG_model)
noise_C00 = noises.sigma[0]
noise_C22 = noises.sigma[1]
noise_C20 = noises.sigma[2]
# C00_I_list, C22_I_list, C20_I_list, Cmm_I_list, C0m_I_list, C2m_I_list
# C00, C22, C20, C0m, C2m, Cmm,
proptoDE_fisher = fisher_cov(f_Pk_RSD_DE_mono, f_Pk_RSD_DE_quad, Survey1, 'DE', noises.k_eff, 0.01, noise_C00, noise_C22, noise_C20, 0.63, 0.2)
# (f, f2, f3 Survey, model, xval, step, C00, C22, C20, C0m, C2m, Cmm, *args)
elif model_selection == 'a':
MG_model.set_model('a', 1, 0.48, 0.27, 0, 1) #change to set DE or a model
MG_model.compute_MG_Pk(z_target, kList)
noises = Noise_Model(Pn, P_shot, alpha, l_knee, Beta, eta_min, target_line_intensity)
noises.set_Noise_model('covariance_full', Survey1, MG_model)
noise_C00 = noises.sigma[0]
noise_C22 = noises.sigma[1]
noise_C20 = noises.sigma[2]
proptoDE_fisher = fisher_cov(f_Pk_RSD_a_mono, f_Pk_RSD_a_quad, Survey1, 'a', noises.k_eff, 0.01, noise_C00, noise_C22, noise_C20, 0.48, 0.27)
else:
raise ValueError('Modified Gravity Model must be a or DE.')
return proptoDE_fisher
# In[7]:
def main():
"""
main function of the script, sets and initializes various parameters, prints files to log etc.
This computes the Fisher matrix as the sum of each target line in each bandwidth window.
The full forecast will use all target lines in all bandwidth windows, e.g. the sum of the sum of Fisher matrices calculated here.
"""
import sys
if len(sys.argv) > 2:
print(len(sys.argv))
print('Number of arguments:', len(sys.argv), 'arguments.')
print('Argument List:', str(sys.argv))
else:
raise ValueError('Insufficient number of arguments.')
Btar = sys.argv[1]
R = sys.argv[5]
if Btar == str(90):
B_tar = 25
delta_nu = 90 / float(R)
print('delta_nu is: ')
print(delta_nu)
# target lines and redshifts
nu_rest_tar_list = [115, 230, 345]
b_list = [b_10_90, b_21_150, b_32_150]
z_list = [z_10_90, b_21_150, b_21_150]
P_shot_list = [I10, I21, I32_26]
# interloper lines at those target frequencies and redshifts
z_in_list = [z_10_90, z_21_90, z_32_90, z_43_90, z_54_90, z_65_90, z_76_90]
b_in_list = [b_10_90, b_21_90, b_32_90, b_43_90, b_54_90, b_65_90, b_76_90]
# nu_rest_tar_list = [nu_10, nu_21, nu_32, nu_43, nu_54, nu_65, nu_76]
# b_list = b_in_list
# z_list = z_in_list
elif Btar == str(150):
B_tar = 50
delta_nu = 150 / float(R)
print('delta_nu is: ')
print(delta_nu)
# target lines and redshifts
nu_rest_tar_list = [230, 345]
b_list = [b_21_150, b_32_150]
z_list = [b_21_150, b_32_150]
P_shot_list = [I21, I32_13]
# interloper lines at those target frequencies and redshifts
z_in_list = [z_21_150, z_21_150, z_43_150, z_54_150, z_65_150, z_76_150, z_98_150]
b_in_list = [b_21_150, b_32_150, b_43_150, b_54_150, b_65_150, b_76_150, b_98_150]
# nu_rest_tar_list = [nu_21, nu_32, nu_43, nu_54, nu_65, nu_76, nu_98]
# b_list = b_in_list
# z_list = z_in_list
elif Btar == str(245):
B_tar = 100
delta_nu = 245 / float(R)
print('delta_nu is: ')
print(delta_nu)
# target lines and redshifts
z_list = [z_32_245]
b_list = [b_32_245]
P_shot_list = [I32_04]
# # z_in_list = [z_32_245, z_43_245, z_54_245, z_65_245, z_76_245, z_98_245]
# # b_in_list = [b_32_245, b_43_245, b_54_245, b_65_245, b_76_245, b_98_245]
nu_rest_tar_list = [345]
# interloper lines at those target frequencies and redshifts