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inf_mma [2023/06/16 14:13]
theoastro |
inf_mma [2023/06/16 14:28] (current)
theoastro |
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| Moreover, a prior on all observed messengers is required and needs to be tailored to the models used in the inference. Here, we use the GRB afterglow light curve model ''TrPi2018'' from afterglowpy and the kilonova model ''Bu2019lm''. A prior for the joint inference can be found [[https://github.com/nuclear-multimessenger-astronomy/nmma/blob/main/example_files/prior/GW170817_AT2017gfo_GRB170817A.prior|here]], called ''GW170817_AT2017gfo_GRB170817A.prior''. | Moreover, a prior on all observed messengers is required and needs to be tailored to the models used in the inference. Here, we use the GRB afterglow light curve model ''TrPi2018'' from afterglowpy and the kilonova model ''Bu2019lm''. A prior for the joint inference can be found [[https://github.com/nuclear-multimessenger-astronomy/nmma/blob/main/example_files/prior/GW170817_AT2017gfo_GRB170817A.prior|here]], called ''GW170817_AT2017gfo_GRB170817A.prior''. |
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| | **Electroamagnetic data and models** |
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| | In order to not only sample on gravitational-wave data, we provide further electromagnetic signal related flags. The flag ''with-grb=True'' will turn on the sampling on a GRB data. As NMMA currently only includes one GRB model, this model does not need to be further specified. If ''with-grb=False'', a joint inference of GW+KN data is possible, excluding the GRB part. With regard to the kilonova model, we need to provide a specific model under ''kilonova-model'', its respective reduced model grid (if applicable) under ''kilonova-model-svd'' and a ''kilonova-interpolation-type'' which can be either ''sklearn_gp'' or ''tensorflow''. The ''light-curve-data'' flag should include both GRB and kilonova data if a joint inference on GW-GRB-KN is desired (meaning use: ''with-grb=True'') or should just include the kilonova data if a GW-KN inference is targeted (meaning use: ''with-grb=False''). |
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| **Including EOS information** | **Including EOS information** |
