BrokenPowerLawLightcurve

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

A sharply broken power-law transient with a rise and decline.

\[\begin{split}L(t) = A \begin{cases} \left(\dfrac{t}{t_\mathrm{peak}}\right)^{\alpha_\mathrm{rise}}, & 0 < t \le t_\mathrm{peak}, \\[6pt] \left(\dfrac{t}{t_\mathrm{peak}}\right)^{-\alpha_\mathrm{decline}}, & t > t_\mathrm{peak}. \end{cases}\end{split}\]

Both indices are defined to be positive. The two branches meet exactly at t_peak, where \(L(t_\mathrm{peak}) = A\). For positive rise index, the luminosity tends continuously to zero as \(t \rightarrow 0\).

Parameters

The light curve parameters are summarized below.

Parameter	Symbol	Description
amplitude	\(A\)	Peak bolometric luminosity.
t_peak	\(t_\mathrm{peak}\)	Time of the power-law break and peak.
rise_index	\(\alpha_\mathrm{rise}\)	Positive pre-peak power-law index.
decline_index	\(\alpha_\mathrm{decline}\)	Positive post-peak power-law decline index.

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()