GREDLightcurve

class uvex_transients.models.lightcurves.generic.GREDLightcurve(**overrides: Parameter | Quantity | float | int)

A Gaussian rise followed by an exponential decline, peaked at t_peak.

\[\begin{split}L(t) = A \times \begin{cases} \exp\left(-\dfrac{(t - t_\mathrm{peak})^2}{2\sigma_\mathrm{rise}^2}\right) & t \le t_\mathrm{peak} \\[4pt] \exp\left(-\dfrac{t - t_\mathrm{peak}}{\tau_\mathrm{decline}}\right) & t > t_\mathrm{peak} \end{cases}\end{split}\]

The two pieces agree at \(t = t_\mathrm{peak}\), where \(L(t_\mathrm{peak}) = A\) exactly, by construction. t_peak is not itself a free parameter – it is fixed at \(t_\mathrm{peak} = 5\sigma_\mathrm{rise}\), five Gaussian widths after explosion.

Parameters

The light curve parameters are summarized below.

Parameter	Symbol	Description
amplitude	\(A\)	Peak bolometric luminosity.
sigma_rise	\(\sigma_\mathrm{rise}\)	Gaussian width of the rise.
tau_decline	\(\tau_\mathrm{decline}\)	Exponential decline timescale.

Methods

eval(t, **parameters)	Evaluate the bolometric luminosity at the given time.
eval_cgs(t, **parameters)	Bolometric luminosity, taking and returning plain cgs numbers.
eval_from_arrays(t, *parameters)	Positional-argument form of eval().
eval_log(t, **parameters)	Natural log of the bolometric luminosity, given physical-unit inputs.
eval_log_cgs(t, **parameters)	Natural log of the bolometric luminosity, taking and returning plain cgs numbers.
get(k[,d])
items()
keys()
pack_params_to_arrays(**parameters)	Convert a dict of parameter values into an ordered sequence.
sample_parameters([size, rng, parameters])	Draw random samples of some or all of this model's parameters.
simulate(t[, size, rng])	Draw random parameter realizations and evaluate the model at the given time.
unpack_params_from_arrays(*parameters)	Convert an ordered sequence of parameter values back into a dict.
values()