MoragShockCoolingSED

class uvex_transients.models.supernovae.IIb.MoragShockCoolingSED(**overrides: Parameter | Quantity | float | int)

Full UV-suppressed shock-cooling SED (Eq. A7).

Based on Morag+24[1].

The frequency-dependent SED: line suppression above \(3.5\,T_\mathrm{col}\), plus a free-free correction below it. See MoragShockCoolingBlackbodySED for the simpler pure-blackbody form (Eq. A8) instead.

Notes

Unlike MoragShockCoolingBlackbodySED, this SED has no closed-form bolometric luminosity: Eq. A7’s UV-suppression/free-free reshaping of the underlying blackbody is not constructed to conserve the Eq. A1 diffusion-envelope \(L_\mathrm{bol}(t)\) upon frequency integration (by as much as ~15-20%, depending on epoch), so _eval_bolometric() genuinely needs the frequency integral of _eval() itself, not Eq. A1. A generic adaptive scipy.integrate.quad_vec() over \((0, \infty)\) converges too slowly through Eq. A7’s line-suppression kink to be practical, so this is overridden with a fixed, log-spaced grid in \(h\nu\) (0.1-10⁴ eV, comfortably spanning the Wien tail on both sides of any physically reasonable \(T_\mathrm{col}\)) and a trapezoidal quadrature in \(\ln\nu\) – accurate to <0.2% against an adaptive reference across the whole validity window, and orders of magnitude faster.

References

[1]

Jonathan Morag, Ido Irani, Nir Sapir, and Eli Waxman. Shock cooling emission from explosions of red supergiants: II. An analytic model of deviations from blackbody emission. \mnras , 528(4):7137–7155, March 2024. arXiv:2307.05598, doi:10.1093/mnras/stae374.

Methods

as_astropy_model([x_type, y_type, y_kind, ...])	Build an Model of this SpectralModel for a given parameter set.
as_source_spectrum(t, *[, redshift, ...])	Build a SourceSpectrum giving the observed flux at one fixed time \(t\).
eval(nu, t, **parameters)	Evaluate the spectral luminosity at the given frequency and time.
eval_bolometric(t, **parameters)	Evaluate the bolometric luminosity at the given time.
eval_bolometric_cgs(t, **parameters)	Bolometric luminosity, taking and returning plain cgs numbers.
eval_bolometric_log(t, **parameters)	Natural log of the bolometric luminosity, given physical-unit inputs.
eval_bolometric_log_cgs(t, **parameters)	Natural log of the bolometric luminosity, taking and returning plain cgs numbers.
eval_cgs(nu, t, **parameters)	Spectral luminosity, taking and returning plain cgs numbers.
eval_from_arrays(nu, t, *parameters)	Positional-argument form of eval().
eval_log(nu, t, **parameters)	Natural log of the spectral luminosity, given physical-unit inputs.
eval_log_cgs(nu, t, **parameters)	Natural log of the spectral luminosity, taking and returning plain cgs numbers.
eval_spectrum(nu, t, **parameters)	Evaluate the normalized spectral shape at the given frequency and time.
eval_spectrum_cgs(nu, t, **parameters)	Return the normalized spectral shape as plain cgs numbers; see eval_log_cgs().
eval_spectrum_log(nu, t, **parameters)	Natural log of the normalized spectral shape, given physical-unit inputs.
eval_spectrum_log_cgs(nu, t, **parameters)	Natural log of the normalized spectral shape, taking and returning plain cgs numbers.
flux(nu, t, *[, redshift, ...])	Evaluate the observed flux density at the given frequency and time.
flux_band(nu, throughput, t, *[, redshift, ...])	Evaluate the throughput-weighted mean observed flux density over a band.
flux_band_cgs(nu, throughput, t, redshift, ...)	Band-averaged observed flux density as plain cgs numbers; see flux_band_log_cgs().
flux_band_log(nu, throughput, t, *[, ...])	Natural log of the band-averaged observed flux density, given physical-unit inputs.
flux_band_log_cgs(nu, throughput, t, ...[, ...])	Natural log of the throughput-weighted mean flux density over a band, plain cgs numbers.
flux_bolometric(t, *[, redshift, ...])	Evaluate the observed bolometric flux at the given time.
flux_bolometric_cgs(t, redshift, ...)	Observed bolometric flux, taking and returning plain cgs numbers.
flux_bolometric_log(t, *[, redshift, ...])	Natural log of the observed bolometric flux, given physical-unit inputs.
flux_bolometric_log_cgs(t, redshift, ...)	Natural log of the observed bolometric flux, taking and returning plain cgs numbers.
flux_cgs(nu, t, redshift, luminosity_distance, *)	Observed flux density, taking and returning plain cgs numbers.
flux_log(nu, t, *[, redshift, ...])	Natural log of the observed flux density, given physical-unit inputs.
flux_log_cgs(nu, t, redshift, ...[, ...])	Natural log of the observed flux density, taking and returning plain cgs numbers.
get(k[,d])
items()
keys()
mag(nu, t, *[, redshift, ...])	Evaluate the apparent AB magnitude at the given frequency and time.
mag_band(nu, throughput, t, *[, redshift, ...])	Evaluate the apparent AB magnitude of the band-averaged flux density.
mag_band_cgs(nu, throughput, t, redshift, ...)	Apparent AB magnitude of the band-averaged flux density.
mag_bandpass(bandpass, t, *[, redshift, ...])	Evaluate the apparent AB magnitude of the flux averaged over bandpass.
mag_cgs(nu, t, redshift, luminosity_distance, *)	Apparent AB magnitude: \(m_\mathrm{AB} = -2.5 \log_{10}(F_\nu / F_{\mathrm{AB},0})\).
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, t[, size, rng])	Draw random parameter realizations and evaluate the model at the given frequency and time.
simulate_photometry(t, exptime, detector, ...)	Simulate noisy synthetic photometry of this model at given time(s), against a real detector.
temperature(t, **parameters)	\(T_\mathrm{col}(t)\) in Kelvin -- the color temperature shared by both subclasses.
unpack_params_from_arrays(*parameters)	Convert an ordered sequence of parameter values back into a dict.
values()