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lc_inference [2022/02/21 13:03]
theoastro 		lc_inference [2023/06/10 18:58] (current)
theoastro
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-===== Light curve inference =====+===== Inference of electromagnetic signals =====
  
 NMMA enables to perform parameter estimation in different electromagnetic regimes such as for kilonovae and gamma-ray burst afterglows. Similarly to light curve generation, the following models are available for kilonova inference: ''Bu2019nsbh'' (BH-NS model), ''Bu2019lm''  (BNS model), ''Me2017'' (BNS model), and ''Ka2017'' (BNS model).  NMMA enables to perform parameter estimation in different electromagnetic regimes such as for kilonovae and gamma-ray burst afterglows. Similarly to light curve generation, the following models are available for kilonova inference: ''Bu2019nsbh'' (BH-NS model), ''Bu2019lm''  (BNS model), ''Me2017'' (BNS model), and ''Ka2017'' (BNS model). 
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 This generates a file called injection.json that includes an injection file drawn from the prior file with a number of injections specified by –n-injection. The injection file generated for a certain kilonova model,  (see also here) can be used to start a Bayesian inference analysis for kilonovae.  The Bayesian inference can be started using: This generates a file called injection.json that includes an injection file drawn from the prior file with a number of injections specified by –n-injection. The injection file generated for a certain kilonova model,  (see also here) can be used to start a Bayesian inference analysis for kilonovae.  The Bayesian inference can be started using:
  
-  light_curve_analysis --model Me2017 --svd-path ./svdmodels --outdir outdir --label injection --prior priors/Me2017.prior --tmin 0.1 --tmax 20 --dt 0.5 --error-budget 1 --nlive 512 --Ebv-max 0 --injection ./injection.json --injection-num 0 --injection-outfile outdir/lc.csv --generation-seed 42 --filters u,g,r,i,z,y,J,H,K --plot --remove-nondetections+  light_curve_analysis --model Me2017 --svd-path ./svdmodels --interpolation_type sklearn_gp --outdir outdir --label injection --prior priors/Me2017.prior --tmin 0.1 --tmax 20 --dt 0.5 --error-budget 1 --nlive 512 --Ebv-max 0 --injection ./injection.json --injection-num 0 --injection-outfile outdir/lc.csv --generation-seed 42 --filters u,g,r,i,z,y,J,H,K --plot --remove-nondetections
  
 A result of obtained posteriors is shown below: A result of obtained posteriors is shown below:
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 As for the light curve generation, the ''Bu2019lm'' model requires a SVD grid as input and can be found [[https://github.com/nuclear-multimessenger-astronomy/nmma/tree/main/svdmodels|here]]. Eventually, the Bayesian inference for kilonova observational data can then be started using: As for the light curve generation, the ''Bu2019lm'' model requires a SVD grid as input and can be found [[https://github.com/nuclear-multimessenger-astronomy/nmma/tree/main/svdmodels|here]]. Eventually, the Bayesian inference for kilonova observational data can then be started using:
-  mpiexec -np 16 light_curve_analysis --model Bu2019lm --svd-path nmma/svdmodels/ --outdir outdir --label AT2017gfo_Bu2019lm --trigger-time 57982.5285236896 --data ./AT2017gfo.dat --prior ./AT2017gfo.prior --tmin 0 --tmax 14 --dt 0.1 --error-budget 1 --nlive 1024 --Ebv-max 0+  mpiexec -np 16 light_curve_analysis --model Bu2019lm --svd-path nmma/svdmodels/ --interpolation_type sklearn_gp --outdir outdir --label AT2017gfo_Bu2019lm --trigger-time 57982.5285236896 --data ./AT2017gfo.dat --prior ./AT2017gfo.prior --tmin 0 --tmax 14 --dt 0.1 --error-budget 1 --nlive 1024 --Ebv-max 0
  
 A result plot could look like the example shown below: A result plot could look like the example shown below:
Last modified: le 2022/02/21 13:03

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