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Combined light curve inference
Whereas the previous section dealt with stand-alone Bayesian inferences with different models (kilonova, GRB afterglow), NMMA enables to run a combined inference using multiple models. Below, we show examples for 2 different types of sources:
- Binary Neutron Star (BNS)
- Neutron-Star-Black-Hole (NSBH)
Here, we use the observed GRB211211A signal as a case study to run Bayesian inference with multiple models. In principle, this sort of signal can originate from a BNS source, but also a NSBH source could be explain the observed signature.
Example: BNS source
If we assume a BNS source, we can use the model TrPi2018 for modelling Gamma-ray burst afterglows along with the kilonova model Bu2019lm. To run a joint inference with these 2 models, we use the observed data GRB211211A.txt, an adapted prior file which includes prior settings for both models and the SVD grid for the Bu2019lm kilonova model. The joint inference can be started using the command:
mpiexec -np 16 light_curve_analysis --model Bu2019lm,TrPi2018 --svd-path ./ --interpolation_type sklearn_gp --outdir outdir --label GRB211211A --prior ./Bu2019lm_TrPi2018GRB211211A.prior --tmin 0.1 --tmax 14 --dt 0.1 --error-budget 1 --nlive 1024 --Ebv-max 0 --trigger-time 59559.54791666667 --data ./GRB211211A.txt --plot --filters u,g,r,i,J,K --xlim 0,14 --ylim 26,16
A potential afterglow of a kilonova could for example be analyzed with Bu2019lm and the GRB afterglow with the model TrPi2018
Example: NSBH source
source: kilonova analyzed with Bu2019lm and GRB afterglow anaylzed with TrPi2018
