Tutorials

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 models.py from time to time.

These are optical counterparts to binary neutron star mergers generated by r-process material produced (Metzger 2017). In this framework, we use a POSSIS-based grid of kilonova models spanning the plausible binary neutron star parameter space (Dietrich et al. 2020). There are four parameters:

• the dynamical ejecta mass \(M_{ej}^{dyn}\) ,
• the wind ejecta mass \(M_{ej}^{wind}\) ,
• the half opening angle \(\phi\) ,
• the observatoin angle \(\theta_{obs}\)

We use afterglowpy (Ryan et al. 2020), an open-source computational tool modeling forward shock synchrotron emission from relativistic blast waves as a function of jet structure and viewing angle. The model parameters are:

• the isotropic kinetic energy \(E_{K, iso}\) ,
• the jet collimation angle \(\theta_{c}\) ,
• the viewing angle \(\theta_{v}\) ,
• the circumburst constant density \(n\),
• the spectral slope of the electron distribution \(p\) ,
• the fraction of energy imparted to the electrons by the shock \(\epsilon_{e}\) ,
• the fraction of energy imparted to the magnetic field \(\epsilon_{B}\),

We use a model from Piro et al. 2021. Following shock breakout, the radiation of shock heated material expands and cools, known as shock cooling emission. The model has parameters:

• the mass of extended material \(M_{e}\),
• the radius of extended material \(R_{e}\),
• the energy of material as the shock passes through it \(E_{e}\)

We rely on a few different models for supernovae from sncosmo. For example, the nugent-hyper model (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.

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 here