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dmeff_n0_1TeV.yaml
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dmeff_n0_1TeV.yaml
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theory:
classy:
path: /home1/davidvng/repos/class_dmeff
extra_args:
#non linear: halofit
#hmcode doesn't exist in dmeff class
#hmcode_min_k_max: 20
N_ncdm: 1
N_ur: 2.0328
likelihood:
planck_2018_highl_plik.TTTEEE_lite: null
planck_2018_lensing.clik: null
params:
logA:
prior:
min: 1.61
max: 3.91
ref:
dist: norm
loc: 3.05
scale: 0.001
proposal: 0.001
latex: \log(10^{10} A_\mathrm{s})
drop: true
A_s:
value: 'lambda logA: 1e-10*np.exp(logA)'
latex: A_\mathrm{s}
n_s:
prior:
min: 0.8
max: 1.2
ref:
dist: norm
loc: 0.965
scale: 0.004
proposal: 0.002
latex: n_\mathrm{s}
theta_s_1e2:
prior:
min: 0.5
max: 10
ref:
dist: norm
loc: 1.0416
scale: 0.0004
proposal: 0.0002
latex: 100\theta_\mathrm{s}
drop: true
100*theta_s:
value: 'lambda theta_s_1e2: theta_s_1e2'
derived: false
H0:
latex: H_0
omega_b:
prior:
min: 0.005
max: 0.1
ref:
dist: norm
loc: 0.0224
scale: 0.0001
proposal: 0.0001
latex: \Omega_\mathrm{b} h^2
omega_cdm:
value: 1.0e-20
omega_dmeff:
prior:
min: 0.001
max: 0.99
ref:
dist: norm
loc: 0.12
scale: 0.001
proposal: 0.0005
latex: \Omega_\mathrm{dmeff} h^2
m_dmeff:
value: 1.0e3
latex: m_\mathrm{dmeff}
npow_dmeff:
value: 0
latex: npow_\mathrm{dmeff}
sigma_dmeff:
prior:
min: 1.0e-30
max: 1.0e-21
# ref:
# dist: uniform
# min: 1.0e-30
# max: 1.0e-22
# proposal: 0.0005
latex: \sigma_\mathrm{dmeff}
Omega_m:
latex: \Omega_\mathrm{m}
omegamh2:
derived: 'lambda Omega_m, H0: Omega_m*(H0/100)**2'
latex: \Omega_\mathrm{m} h^2
m_ncdm:
value: 0.06
renames: mnu
Omega_Lambda:
latex: \Omega_\Lambda
YHe:
latex: Y_\mathrm{P}
tau_reio:
prior:
min: 0.01
max: 0.8
ref:
dist: norm
loc: 0.055
scale: 0.006
proposal: 0.003
latex: \tau_\mathrm{reio}
z_reio:
latex: z_\mathrm{re}
sigma8:
latex: \sigma_8
s8h5:
derived: 'lambda sigma8, H0: sigma8*(H0*1e-2)**(-0.5)'
latex: \sigma_8/h^{0.5}
s8omegamp5:
derived: 'lambda sigma8, Omega_m: sigma8*Omega_m**0.5'
latex: \sigma_8 \Omega_\mathrm{m}^{0.5}
s8omegamp25:
derived: 'lambda sigma8, Omega_m: sigma8*Omega_m**0.25'
latex: \sigma_8 \Omega_\mathrm{m}^{0.25}
A:
derived: 'lambda A_s: 1e9*A_s'
latex: 10^9 A_\mathrm{s}
clamp:
derived: 'lambda A_s, tau_reio: 1e9*A_s*np.exp(-2*tau_reio)'
latex: 10^9 A_\mathrm{s} e^{-2\tau}
age:
latex: '{\rm{Age}}/\mathrm{Gyr}'
rs_drag:
latex: r_\mathrm{drag}
sampler:
mcmc:
covmat: auto
drag: false
oversample_power: 0.4
proposal_scale: 1.9
output: chains/dmeff_n0_1TeV
#hmcode doesn't exist in dmeff class; set non linear to halofit and removed hmcode_min_k_max
#leaving drag as true ended with seg faults each time
#leaving non linear as empty seems to improve memory a little bit