def initialise_model(redshift, model_name='zheng07'):
print('Initialising HOD model: {}...'.format(model_name))
# Default is ‘halo_mvir’
if model_name == 'zheng07':
model = PrebuiltHodModelFactory('zheng07',
redshift=redshift,
threshold=-18,
prim_haloprop_key=halo_m_prop)
model.param_dict['logMmin'] = 13.3
model.param_dict['sigma_logM'] = 0.8
model.param_dict['alpha'] = 1
model.param_dict['logM0'] = 13.3
model.param_dict['logM1'] = 13.8
elif model_name == 'leauthaud11':
model = PrebuiltHodModelFactory('leauthaud11',
redshift=redshift,
threshold=11,
prim_haloprop_key=halo_m_prop)
model.param_dict['smhm_m0_0'] = 11.5 # 10.72
model.param_dict['smhm_m0_a'] = 0.59
model.param_dict['smhm_m1_0'] = 13.4 # 12.35
model.param_dict['smhm_m1_a'] = 0.3
model.param_dict['smhm_beta_0'] = 2 # 0.43
model.param_dict['smhm_beta_a'] = 0.18
model.param_dict['smhm_delta_0'] = 0.1 # 0.56
model.param_dict['smhm_delta_a'] = 0.18
model.param_dict['smhm_gamma_0'] = 1 # 1.54
model.param_dict['smhm_gamma_a'] = 2.52
model.param_dict['scatter_model_param1'] = 0.2
model.param_dict['alphasat'] = 1
model.param_dict['betasat'] = 1.1 # 0.859
model.param_dict['bsat'] = 11 # 10.62
model.param_dict['betacut'] = 6 # -0.13
model.param_dict['bcut'] = 0.01 # 1.47
elif model_name == 'tinker13':
model = PrebuiltHodModelFactory(
'tinker13',
redshift=redshift,
threshold=11,
prim_haloprop_key=halo_m_prop,
quiescent_fraction_abscissa=[1e12, 1e13, 1e14, 1e15],
quiescent_fraction_ordinates=[0.25, 0.5, 0.75, 0.9])
model.param_dict['smhm_m0_0_active'] = 11
model.param_dict['smhm_m0_0_quiescent'] = 10.8
model.param_dict['smhm_m1_0_active'] = 12.2
model.param_dict['smhm_m1_0_quiescent'] = 11.8
model.param_dict['smhm_beta_0_active'] = 0.44
model.param_dict['smhm_beta_0_quiescent'] = 0.32
# model.param_dict['alphasat_active'] = 1
# model.param_dict['alphasat_quiescent'] = 1
# model.param_dict['betacut_active'] = 0.77
# model.param_dict['betacut_quiescent'] = -0.12
# model.param_dict['bcut_active'] = 0.2
# model.param_dict['bcut_quiescent'] = 0.2
# model.param_dict['betasat_active'] = 1.5
# model.param_dict['betasat_quiescent'] = 0.62
model.param_dict['bsat_active'] = 13
model.param_dict['bsat_quiescent'] = 8
elif model_name == 'hearin15':
model = PrebuiltHodModelFactory('hearin15',
redshift=redshift,
threshold=11)
elif model_name == 'zu_mandelbaum15':
model = PrebuiltHodModelFactory('zheng07',
redshift=redshift,
threshold=-1)
elif model_name == 'zu_mandelbaum16':
model = PrebuiltHodModelFactory('zheng07',
redshift=redshift,
threshold=-1)
elif model_name == 'cacciato09':
model = PrebuiltHodModelFactory('cacciato09',
redshift=redshift,
threshold=10,
prim_haloprop_key=halo_m_prop)
model.param_dict['log_L_0'] = 9.935
model.param_dict['log_M_1'] = 12.9 # 11.07
model.param_dict['gamma_1'] = 0.3 # 3.273
model.param_dict['gamma_2'] = 0.255
model.param_dict['sigma'] = 0.143
model.param_dict['a_1'] = 0.501
model.param_dict['a_2'] = 2.106
model.param_dict['log_M_2'] = 14.28
model.param_dict['b_0'] = -0.5 # -0.766
model.param_dict['b_1'] = 1.008
model.param_dict['b_2'] = -0.094
model.param_dict['delta_1'] = 0
model.param_dict['delta_2'] = 0
# add useful model properties here
model.model_name = model_name # add model_name field
return model
def initialise_model(redshift, model_name, halo_m_prop='halo_m'):
'''
create an instance of halotools model class
logMcut : The cut-off mass for the halo to host in a central galaxy. Given
in solar mass.
sigma_lnM : Parameter that modulates the shape of the number of
central galaxies.
logM1 : The scale mass for the number of satellite galaxies.
kappa : Parameter that affects the cut-off mass for satellite galaxies.
A detailed discussion and best-fit values of these parameters can be found
in Zheng+2009.
alpha : The power law index of the number of satellite galaxies.
alpha_c : float. The central velocity bias parameter. Modulates the
peculiar velocity of the central galaxy. The larger the absolute value of
the parameter, the larger the peculiar velocity of the central. The sign
of the value does not matter.
s : The satellite profile modulation parameter. Modulates how the radial
distribution of satellite galaxies within halos deviate from the radial
profile of the halo. Positive value favors satellite galaxies to populate
the outskirts of the halo whereas negative value favors satellite galaxy
to live near the center of the halo. |s| must be < 1.
s_v : float. The satellite velocity bias parameter. Modulates how the
satellite galaxy peculiar velocity deviates from that of the local dark
matter particle. Positive value favors high peculiar velocity satellite
galaxies and vice versa. Note that our implementation preserves the
Newton's second law of the satellite galaxies. |s| must be < 1.
s_p : float. The perihelion distance modulation parameter. A positive
value favors satellite galaxies to have larger distances to the halo
center upon their closest approach to the center and vice versa. This can
be regarded as a "fancier" satellite profile modulation parameter.
|s| must be < 1.
A : float. The assembly bias parameter. Introduces the effect of assembly
bias. A positive value favors higher concentration halos to host galaxies
whereas a negative value favors lower concentration halos to host galaxies.
If you are invoking assembly bias decoration, i.e. a non-zero A parameter,
you need to run gen_medianc.py first. A detailed discussion of these
parameters can be found in Yuan et al. in prep. To turn off any of the five
decorations, just set the corresponding parameter to 0.
'''
# print('Initialising HOD model: {}...'.format(model_name))
# halotools prebiult models
if model_name == 'matter':
model_type = 'matter'
model = PrebuiltHodModelFactory('zheng07',
redshift=redshift,
threshold=-18,
prim_haloprop_key=halo_m_prop)
model.param_dict['logMmin'] = 13.3
model.param_dict['sigma_logM'] = 0.8
model.param_dict['alpha'] = 1
model.param_dict['logM0'] = 13.3
model.param_dict['logM1'] = 13.8
elif model_name in PrebuiltHodModelFactory.prebuilt_model_nickname_list:
model_type = 'prebuilt'
if model_name == 'zheng07':
model = PrebuiltHodModelFactory('zheng07',
redshift=redshift,
threshold=-18,
prim_haloprop_key=halo_m_prop)
model.param_dict['logMmin'] = 13.3
model.param_dict['sigma_logM'] = 0.8
model.param_dict['alpha'] = 1
model.param_dict['logM0'] = 13.3
model.param_dict['logM1'] = 13.8
elif model_name == 'cacciato09':
model = PrebuiltHodModelFactory('cacciato09',
redshift=redshift,
threshold=10,
prim_haloprop_key=halo_m_prop)
model.param_dict['log_L_0'] = 9.935
model.param_dict['log_M_1'] = 12.9 # 11.07
model.param_dict['gamma_1'] = 0.3 # 3.273
model.param_dict['gamma_2'] = 0.255
model.param_dict['sigma'] = 0.143
model.param_dict['a_1'] = 0.501
model.param_dict['a_2'] = 2.106
model.param_dict['log_M_2'] = 14.28
model.param_dict['b_0'] = -0.5 # -0.766
model.param_dict['b_1'] = 1.008
model.param_dict['b_2'] = -0.094
model.param_dict['delta_1'] = 0
model.param_dict['delta_2'] = 0
elif model_name == 'leauthaud11':
model = PrebuiltHodModelFactory('leauthaud11',
redshift=redshift,
threshold=11,
prim_haloprop_key=halo_m_prop)
model.param_dict['smhm_m0_0'] = 11.5 # 10.72
model.param_dict['smhm_m0_a'] = 0.59
model.param_dict['smhm_m1_0'] = 13.4 # 12.35
model.param_dict['smhm_m1_a'] = 0.3
model.param_dict['smhm_beta_0'] = 2 # 0.43
model.param_dict['smhm_beta_a'] = 0.18
model.param_dict['smhm_delta_0'] = 0.1 # 0.56
model.param_dict['smhm_delta_a'] = 0.18
model.param_dict['smhm_gamma_0'] = 1 # 1.54
model.param_dict['smhm_gamma_a'] = 2.52
model.param_dict['scatter_model_param1'] = 0.2
model.param_dict['alphasat'] = 1
model.param_dict['betasat'] = 1.1 # 0.859
model.param_dict['bsat'] = 11 # 10.62
model.param_dict['betacut'] = 6 # -0.13
model.param_dict['bcut'] = 0.01 # 1.47
elif model_name == 'tinker13':
model = PrebuiltHodModelFactory(
'tinker13',
redshift=redshift,
threshold=11,
prim_haloprop_key=halo_m_prop,
quiescent_fraction_abscissa=[1e12, 1e13, 1e14, 1e15],
quiescent_fraction_ordinates=[0.25, 0.5, 0.75, 0.9])
model.param_dict['smhm_m0_0_active'] = 11
model.param_dict['smhm_m0_0_quiescent'] = 10.8
model.param_dict['smhm_m1_0_active'] = 12.2
model.param_dict['smhm_m1_0_quiescent'] = 11.8
model.param_dict['smhm_beta_0_active'] = 0.44
model.param_dict['smhm_beta_0_quiescent'] = 0.32
# model.param_dict['alphasat_active'] = 1
# model.param_dict['alphasat_quiescent'] = 1
# model.param_dict['betacut_active'] = 0.77
# model.param_dict['betacut_quiescent'] = -0.12
# model.param_dict['bcut_active'] = 0.2
# model.param_dict['bcut_quiescent'] = 0.2
# model.param_dict['betasat_active'] = 1.5
# model.param_dict['betasat_quiescent'] = 0.62
model.param_dict['bsat_active'] = 13
model.param_dict['bsat_quiescent'] = 8
elif model_name == 'hearin15':
model = PrebuiltHodModelFactory('hearin15',
redshift=redshift,
threshold=11)
elif model_name == 'zu_mandelbaum15':
model = PrebuiltHodModelFactory('zheng07',
redshift=redshift,
threshold=-1)
elif model_name == 'zu_mandelbaum16':
model = PrebuiltHodModelFactory('zheng07',
redshift=redshift,
threshold=-1)
# generalised HOD models with 5-parameter zheng07 as base model
else: # 'gen_base1'
model_type = 'general'
model = PrebuiltHodModelFactory('zheng07',
redshift=redshift,
threshold=-18,
prim_haloprop_key=halo_m_prop)
# five baseline parameters
model.param_dict['logMcut'] = 13.35
model.param_dict['sigma_lnM'] = 0.85
model.param_dict['kappa'] = 1
model.param_dict['logM1'] = 13.8
model.param_dict['alpha'] = 1
# decoration parameters
model.param_dict['s'] = 0 # sat ranking by halo centric distance
model.param_dict['s_v'] = 0 # sat ranking by relative speed
model.param_dict['s_p'] = 0 # sat ranking perihelion distance
model.param_dict['alpha_c'] = 0 # centrals velocity bias
model.param_dict['A_cen'] = 0 # centrals assembly bias, pseudomass
model.param_dict['A_sat'] = 0 # satellites assembly bias, pseudomass
''' BOOKKEEPING: DO NOT MODIFY EXISTING MODELS, CREATE NEW ONES '''
if model_name == 'gen_base2': # params are tweaked to produce same ND
model.param_dict['logM1'] = 13.85
model.param_dict['alpha'] = 1.377
elif model_name == 'gen_base3':
model.param_dict['logM1'] = 13.9
model.param_dict['alpha'] = 1.663
elif model_name == 'gen_base4':
model.param_dict['logM1'] = 13.796
model.param_dict['alpha'] = 0.95
elif model_name == 'gen_base5':
model.param_dict['logM1'] = 13.805
model.param_dict['alpha'] = 1.05
elif model_name == 'gen_base6':
model.param_dict['logM1'] = 13.770
model.param_dict['alpha'] = 0.75
elif model_name == 'gen_base7':
model.param_dict['logM1'] = 13.848
model.param_dict['alpha'] = 1.25
elif model_name == 'gen_ass1':
model.param_dict['A_cen'] = 1
model.param_dict['A_sat'] = 0
elif model_name == 'gen_ass2':
model.param_dict['A_cen'] = 0
model.param_dict['A_sat'] = 1
elif model_name == 'gen_ass3':
model.param_dict['A_cen'] = 1
model.param_dict['A_sat'] = 1
elif model_name == 'gen_ass1_n':
model.param_dict['A_cen'] = -1
model.param_dict['A_sat'] = 0
elif model_name == 'gen_ass2_n':
model.param_dict['A_cen'] = 0
model.param_dict['A_sat'] = -1
elif model_name == 'gen_ass3_n':
model.param_dict['A_cen'] = -1
model.param_dict['A_sat'] = -1
elif model_name == 'gen_vel1':
model.param_dict['alpha_c'] = 1
elif model_name == 'gen_vel2':
model.param_dict['alpha_c'] = 0.2
elif model_name == 'gen_s1':
model.param_dict['s'] = 0.9
elif model_name == 'gen_sv1':
model.param_dict['s_v'] = 0.9
elif model_name == 'gen_sp1':
model.param_dict['s_p'] = 0.9
elif model_name == 'gen_s1_n':
model.param_dict['s'] = -0.9
elif model_name == 'gen_sv1_n':
model.param_dict['s_v'] = -0.9
elif model_name == 'gen_sp1_n':
model.param_dict['s_p'] = -0.9
elif model_name == 'gen_allbiases':
model.param_dict['A_cen'] = 1
model.param_dict['A_sat'] = 1
model.param_dict['alpha_c'] = 1
model.param_dict['s'] = 0.9
model.param_dict['s_v'] = 0.9
model.param_dict['s_p'] = 0.9
elif model_name == 'gen_allbiases_n':
model.param_dict['A_cen'] = -1
model.param_dict['A_sat'] = -1
model.param_dict['alpha_c'] = 1
model.param_dict['s'] = -0.9
model.param_dict['s_v'] = -0.9
model.param_dict['s_p'] = -0.9
# add useful model properties here
model.model_name = model_name
model.model_type = model_type
model.halo_m_prop = halo_m_prop
return model