PowerLawSpectrum#

class uvex_transients.models.spectra.powerlaw.PowerLawSpectrum(**overrides: Parameter | Quantity | float | int)[source]#

Finite-band power-law spectral shape.

Represents \(S(\nu) = \nu_0^{-1}(\nu/\nu_0)^\alpha\) between frequency_min and frequency_max, and zero outside that interval. spectral_index is the usual frequency-space index \(\alpha\) defined by \(S(\nu) \propto \nu^\alpha\).

The default spectral_index is sampled from a Gaussian prior centered on -1. reference_frequency and the cutoffs use constant priors by default, making them deterministic unless replaced or overridden explicitly.

Parameters

The spectral shape parameters are summarized below.

Parameter	Symbol	Description
`spectral_index`	\(\alpha\)	Frequency-space power-law index, S(nu) proportional to nu^alpha.
`reference_frequency`	\(\nu_0\)	Reference frequency anchoring the power-law shape.
`frequency_min`	\(\nu_{\min}\)	Lower frequency cutoff of the power-law spectrum.
`frequency_max`	\(\nu_{\max}\)	Upper frequency cutoff of the power-law spectrum.

Methods

`eval`(nu, **parameters)	Evaluate the spectral shape at the given frequency.
`eval_cgs`(nu, **parameters)	Spectral shape, taking and returning plain cgs numbers.
`eval_from_arrays`(nu, *parameters)	Positional-argument form of `eval()`.
`eval_log`(nu, **parameters)	Natural log of the spectral shape, given physical-unit inputs.
`eval_log_cgs`(nu, **parameters)	Natural log of the spectral shape, taking and returning plain cgs numbers.
`eval_normalization`(**parameters)	Evaluate \(\int S(\nu)\,d\nu\).
`eval_normalization_cgs`(**parameters)	Return the shape's frequency integral as plain cgs numbers; see `eval_normalization_log_cgs()`.
`eval_normalization_log`(**parameters)	Natural log of the shape's frequency integral, given physical-unit inputs.
`eval_normalization_log_cgs`(**parameters)	Natural log of the shape's frequency integral, 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`(nu[, size, rng])	Draw random parameter realizations and evaluate the model at the given frequency.
`unpack_params_from_arrays`(*parameters)	Convert an ordered sequence of parameter values back into a dict.
`values`()