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models_nmma [2023/06/10 19:36]
theoastro 		models_nmma [2023/06/16 10:32] (current)
theoastro
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 ===== Available models ===== ===== Available models =====
  
-NMMA will be under continuous development meaning that new astrophysical models such as for kilonovae, supernovae, gamma-ray bursts and so on will be implemented over time. Therefore, it is worthwhile to check out the`model_parameters_dict` in [[https://github.com/nuclear-multimessenger-astronomy/nmma/blob/538326458e1e6101389e9861ff6bf19bc93fa070/nmma/em/model.py#L17|models.py]]  and+NMMA will be under continuous development meaning that new astrophysical models such as for kilonovae, supernovae, gamma-ray bursts and so on will be implemented over time. Therefore, it is worthwhile to check out the ''model_parameters_dict'' in [[https://github.com/nuclear-multimessenger-astronomy/nmma/blob/538326458e1e6101389e9861ff6bf19bc93fa070/nmma/em/model.py#L17|models.py]]  from time to time.
  
 === Kilonovae  === === Kilonovae  ===
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 === Supernovae  === === Supernovae  ===
 We rely on a few different models for supernovae from [[https://github.com/sncosmo/sncosmo|sncosmo]]. For example, the nugent-hyper model [[https://arxiv.org/pdf/astro-ph/0403450|(Levan et al. 2005)]] used for SN Ib/c supernovae with the stretch and scale set to match the intrinsic (dereddened, rest frame) -band luminosity of SN 1998bw at maximum light. The main free parameter is the absolute magnitude. We rely on a few different models for supernovae from [[https://github.com/sncosmo/sncosmo|sncosmo]]. For example, the nugent-hyper model [[https://arxiv.org/pdf/astro-ph/0403450|(Levan et al. 2005)]] used for SN Ib/c supernovae with the stretch and scale set to match the intrinsic (dereddened, rest frame) -band luminosity of SN 1998bw at maximum light. The main free parameter is the absolute magnitude.
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 +Some models are analytic / semi-analytic that can be sampled, others rely on sampling from a grid of modeled light curves through the use of Principle Component Analysis (PCA) and an interpolation scheme (either Gaussian process modeling or neural networks). Further information on model training can be found [[https://enlil.gw.physik.uni-potsdam.de/dokuwiki/doku.php?id=model_grids|here]]
Last modified: le 2023/06/10 19:36

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