TwoComponentBazinLightcurve

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

A superposition of two independent Bazin pulses.

\[L(t) = A_0\, \frac{\exp[-(t-t_0)/\tau_{\mathrm{fall},0}]}{1 + \exp[-(t-t_0)/\tau_{\mathrm{rise},0}]} + A_1\, \frac{\exp[-(t-t_1)/\tau_{\mathrm{fall},1}]}{1 + \exp[-(t-t_1)/\tau_{\mathrm{rise},1}]}.\]

Two ordinary BazinLightcurve pulses, added rather than multiplied, each with its own amplitude, transition time, and rise/fall timescales. Because the two components are independent and additive, this covers both single- and double-peaked light curves in the same functional form: with \(A_0 \ll A_1\) (or a comparable but much earlier/narrower first pulse well clear of the second), the second component alone sets the observed shape – a single peak; with \(A_0\) comparable to \(A_1\), both pulses are visible, with a dip between them where each has decayed enough to let the other dominate. Since the two peaks are genuinely independent components rather than one shape reweighted by another, their relative amplitude, timing, and widths can be tuned separately to fit real double- and single-peaked events alike.

Parameters

The light curve parameters are summarized below.

Parameter	Symbol	Description
amplitude_0	\(A_0\)	Luminosity normalization of the first (typically earlier) pulse.
t0	\(t_0\)	Characteristic transition time of the first pulse.
rise_0	\(\tau_{\mathrm{rise},0}\)	Logistic rise timescale of the first pulse.
fall_0	\(\tau_{\mathrm{fall},0}\)	Exponential decline timescale of the first pulse.
amplitude_1	\(A_1\)	Luminosity normalization of the second (typically later) pulse.
t1	\(t_1\)	Characteristic transition time of the second pulse.
rise_1	\(\tau_{\mathrm{rise},1}\)	Logistic rise timescale of the second pulse.
fall_1	\(\tau_{\mathrm{fall},1}\)	Exponential decline timescale of the second pulse.

See also

BazinLightcurve

The single-pulse form this combines two of.

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()