tdcosmo
Likelihood of the TDCOSMO analyses
File |
likelihood/tdcosmo/tdcosmo_likelihood.py |
Attribution |
TDCOSMO 2025 et al. (https://arxiv.org/abs/2506.03023) |
TDCOSMO IV (Birrer et al.) for the measurement |
|
Martin Millon, Judit Prat and Simon Birrer for the cosmoSIS implementation |
|
URL |
10.1051/0004-6361/202555801 |
Citations |
TDCOSMO Collaboration et al. 2025, A&A, 704, A63 |
Birrer et al., 2020, A&A, 643, A165 |
|
Suyu et al., 2010, ApJ, 711, 201 |
|
Suyu et al., 2014, ApJ, 788, L35 |
|
Wong et al., 2017, MNRAS, 465, 4895 |
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Birrer et al., 2019, MNRAS, 484, 4726 |
|
Chen et al., 2019, MNRAS, 490, 1743 |
|
Jee et al., 2019, Science, 365, 1134 |
|
Rusu et al., 2020, MNRAS, 498, 1440 |
|
Wong et al., 2020, MNRAS, 498, 1420 |
|
Sahjib et al., 2020, MNRAS, 494, 6072 |
This module contain the likelihood of a 8 time-delay lenses, presented in TDCOSMO 2025 (TDCOSMO collaboration et al., 2025). This module allows us to reproduce the hierarchical inference of the cosmological parameters and of the lens population parameters, which are grouped under the block nuisance_strong_lensing (see details below). For the TDCOSMO 2025 analysis, additional data sets such as ‘SLACS_KCWI’ (11 lenses) and ‘SL2S’ (4 lenses) can be added to the ‘tdcosmo2025’ data set to help constrain these parameters. Adding these 2 data sets requires to make the additional assumption that the lensing galaxy of the 8 TDCOSMO lenses and of the SLACS and SL2S lenses come from the same population of galaxies.
Alternatively one can recover the previous analysis from TDCOSMO IV (Birrer et al., 2020) by choosing the ‘tdcosmo_iv’ analysis option. This analysis is now deprecated in favor of the TDCOSMO 2025 analysis, but is still available for reproducibility purposes. It requires to install hierarc <= 1.1.3. For the TDCOSMO IV analysis, additional data sets such as ‘SLACS_SDSS’ and ‘SLACS_IFU’ can be added to the ‘tdcosmo7’ data set to help constrain these parameters. However, it is NOT recommended to use the ‘SLACS_SDSS’ data set anymore (see TDCOSMO 2025 for details).
Assumptions
Strong lensing modelling details.
Time delay distance structure
Hierarchical inference of the mass model and stellar anisotropy parameters
Setup Parameters
Name |
Type |
Default |
Description |
|---|---|---|---|
analysis |
str |
tdcosmo2025 |
Type of analysis to perform. ‘tdcosmo_iv’ or ‘tdcomso2025’ for the TDCOSMO IV and TDCOSMO 2025 analysis respectively. |
data_sets |
str |
tdcosmo2025 |
Data sets to use. For the ‘tdcosmo_iv’ analysis, choose any combination of ‘tdcosmo7’, ‘SLACS_SDSS’ and ‘SLACS_IFU’. You can use ‘tdcosmo7+SLACS_SDSS’ or ‘tdcosmo7+SLACS_SDSS+SLACS_IFU’ for example. For the ‘tdcosmo2025’ analysis, available data sets are ‘tdcosmo2025’, ‘SLACS_KCWI’ and ‘SL2S’. You can use ‘tdcosmo2025+SLACS_KCWI’ or ‘tdcosmo2025+SLACS_KCWI+SL2S’ for example. |
num_distribution_draws |
int |
200 |
Number of random realisation for kinematic computations. |
distances_computation_module |
str |
astropy |
Module used distance-redshift relation. ‘astropy’ uses standard astropy cosmology w0waCDM. ‘CosmoInterp’ to use the CosmoInterp module of lenstronomy to interpolate. ‘camb’ will use the distances provided by camb to compute Ds, Dd, and Dds. |
Input values
Section |
Name |
Type |
Default |
Description |
|---|---|---|---|---|
cosmological_parameters |
omega_l |
real |
Dark energy density fraction today |
|
h0 |
real |
Hubble parameter H0 (km/s/Mpc) |
||
omega_m |
real |
Dark matter density fraction today |
||
nuisance_strong_lensing |
lambda_mst |
real |
1.0 |
Internal Mass sheet degeneracy parameter |
lambda_mst_sigma |
real |
0.04 |
1-sigma Gaussian scatter in lambda_mst |
|
alpha_lambda |
real |
0.0 |
Slope of lambda_mst with r_eff/theta_E |
|
a_ani |
real |
1.5 |
mean a_ani anisotropy parameter in the Osipkov-Merritt model |
|
a_ani_sigma |
real |
0.3 |
1-sigma Gaussian scatter in a_ani |
|
gamma_pl_RXJ1131 |
real |
1.95 |
Power-law slope of RXJ1131 lens mass profile. Used only in ‘tdcosmo2025’ analysis. The slope will be constrained using JWST NIRSpec IFU kinematics data. |
|
gamma_pl_SLACS_KCWI0 |
real |
2.0 |
Power-law slope of SDSSJ0029-0055 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI1 |
real |
2.0 |
Power-law slope of SDSSJ0037-0942 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI2 |
real |
2.0 |
Power-law slope of SDSSJ1112+0826 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI3 |
real |
2.0 |
Power-law slope of SDSSJ1204+0358 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI4 |
real |
2.0 |
Power-law slope of SDSSJ1250+0523 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI5 |
real |
2.0 |
Power-law slope of SDSSJ1306+0600 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI6 |
real |
2.0 |
Power-law slope of SDSSJ1402+6321 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI7 |
real |
2.0 |
Power-law slope of SDSSJ1531-0105 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI8 |
real |
2.0 |
Power-law slope of SDSSJ1621+3931 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI9 |
real |
2.0 |
Power-law slope of SDSSJ1627-0053 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
|
gamma_pl_SLACS_KCWI10 |
real |
2.0 |
Power-law slope of SDSSJ1630+4520 lens mass profile. Used only in ‘tdcosmo2025’ analysis + ‘SLACS_KCWI’ data_sets. The slope will be constrained using KCWI IFU kinematics data. |
Output values
Section |
Name |
Type |
Description |
|---|---|---|---|
likelihoods |
TDCOSMO_like |
real |
Total likelihood of the TDCOSMO sample |