-
Notifications
You must be signed in to change notification settings - Fork 0
/
jense_2023_pk_camb_mnu.yaml
107 lines (100 loc) · 2.71 KB
/
jense_2023_pk_camb_mnu.yaml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
network_name: jense_2023_camb_mnu
path: jense_2023_camb_mnu
emulated_code:
name: camb
version: "1.5.2"
inputs: [ ombh2, omch2, As, ns, H0, z, NonLinearModel.HMCode_A_baryon, NonLinearModel.HMCode_eta_baryon, NonLinearModel.HMCode_logT_AGN, mnu ]
extra_args:
kmax: 50.0
k_per_logint: 130
AccuracyBoost: 1.0
lAccuracyBoost: 1.2
lSampleBoost: 1.0
DoLateRadTruncation: false
recombination_model: CosmoRec
NonLinearModel.Min_kh_nonlinear: 5.0e-5
NonLinearModel.halofit_version: "mead2020"
samples:
Ntraining: 120000
parameters:
ombh2: [0.015,0.030]
omch2: [0.09,0.15]
logA: [2.5,3.5]
ns: [0.85, 1.05]
h: [0.4,1.0]
# P(k)-specific
z: [0.0, 5.0]
A_b: [2.0, 4.0]
eta_b: [0.5, 1.0]
logT_AGN: [7.3, 8.3]
NonLinearModel.HMCode_A_baryon: "lambda A_b: A_b"
NonLinearModel.HMCode_eta_baryon: "lambda eta_b: eta_b"
NonLinearModel.HMCode_logT_AGN: "lambda logT_AGN: logT_AGN"
H0: "lambda h: h * 100.0"
As: "lambda logA: 1.e-10 * np.exp(logA)"
mnu: [0.0, 0.5]
networks:
- quantity: "Pk/lin"
type: NN
log: True
modes:
label: k
range: [5.e-5, 50.0]
spacing: log
steps: 1000
n_traits:
n_hidden: [ 512, 512, 512, 512 ]
training:
validation_split: 0.1
learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
patience_values: 100
max_epochs: 1000
- quantity: "Pk/nonlin"
type: NN
log: True
modes:
label: k
range: [5.e-5, 50.0]
spacing: log
steps: 1000
n_traits:
n_hidden: [ 512, 512, 512, 512 ]
training:
validation_split: 0.1
learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
patience_values: 100
max_epochs: 1000
- quantity: "Pk/nlboost"
type: NN
log: False
modes:
label: k
range: [5.e-5, 50.0]
spacing: log
steps: 1000
n_traits:
n_hidden: [ 512, 512, 512, 512 ]
training:
validation_split: 0.1
learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
patience_values: 100
max_epochs: 1000
- quantity: "sigma8"
inputs: [ ombh2, omch2, logA, ns, h, A_b, eta_b, logT_AGN, mnu ]
type: NN
log: True
modes:
label: z
range: [0, 5]
steps: 1000
n_traits:
n_hidden: [ 512, 512, 512, 512 ]
training:
validation_split: 0.1
learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
patience_values: 100
max_epochs: 1000