AlushStoneTDESED#

class uvex_transients.models.tdes.alush_stone.AlushStoneTDESED(**overrides: Parameter | Quantity | float | int)[source]#

A Gaussian-rise/exponential-decline photosphere followed by a magnetized-disk plateau.

\[L_\nu(\nu, t) = L_\mathrm{bol}^\mathrm{early}(t) \cdot S(\nu, T) + L_\mathrm{bol}^\mathrm{plat}(t) \cdot S(\nu, T_\mathrm{p}),\]

where \(S(\nu, T)\) is BlackbodySpectrum’s normalized shape, \(L_\mathrm{bol}^\mathrm{early}(t)\) is GREDLightcurve (peaking at \(t_\mathrm{peak} = 5\sigma\), that class’s own convention), and

\[L_\mathrm{bol}^\mathrm{plat}(t) = L_\mathrm{p} \left(1 + \frac{t - t_\mathrm{peak}}{\tau_\mathrm{p}}\right)^{-\alpha_\mathrm{p}}.\]

The plateau branch is referenced to the same \(t_\mathrm{peak}\) as the early branch (the same convention GaussianRisePowerLawLightcurve uses for its power-law tail): \(L_\mathrm{bol}^\mathrm{plat}(t_\mathrm{peak}) = L_\mathrm{p}\) exactly, and it smoothly softens from a flat plateau (for \(t - t_\mathrm{peak} \ll \tau_\mathrm{p}\)) to a \(t^{-\alpha_\mathrm{p}}\) power-law decline (for \(t - t_\mathrm{peak} \gg \tau_\mathrm{p}\)) – exactly the family of theory-agnostic plateau shapes fit to late-time TDE disks by Alush et al.[1]. The two components are summed in linear space (numpy.logaddexp() on their logs) rather than switched between, since a real disk plateau does not sharply replace the fading early-time emission.

Physically, the early branch is the same reprocessed/photospheric emission VanVelzenTDESED models, while the plateau is the late-time, UV-bright emission from a magnetized accretion disk that Alush and Stone[2] predict settles onto an asymptotic \(L \propto t^{-5/6}\) decline persisting for decades to centuries – plateau_decline’s default prior is a wide, uninformative \(\mathrm{Uniform}(0, 2)\) that brackets that theoretical value rather than pinning it, since Alush et al.[1] find real plateaus vary in how flat/evolving they are.

temperature/sigma_rise/tau_decline reuse the same ZTF-sample-informed defaults as VanVelzenTDESED (see that class’s docstring); plateau_temperature, plateau_amplitude, and plateau_timescale do not yet have literature-calibrated defaults and use order-of-magnitude fiducial scales instead – narrow these once fit to real late-time photometry.

Parameters

The model parameters are summarized below.

Parameter	Symbol	Description
`amplitude`	\(L_0\)	Peak bolometric luminosity of the early-time component, \(L_0 = L_\mathrm{bol}^\mathrm{early}(t_\mathrm{peak})\). \(\log_{10}(L_0/\mathrm{erg\,s^{-1}}) \sim \mathcal{N}(43.8, 0.3^2)\).
`temperature`	\(T\)	Early-time photospheric blackbody temperature. \(\log_{10}(T/\mathrm{K}) \sim \mathcal{N}(4.3, 0.1^2)\).
`sigma_rise`	\(\sigma\)	Gaussian width of the pre-peak rise. \(\log_{10}(\sigma/\mathrm{d}) \sim \mathcal{N}(0.91, 0.25^2)\).
`tau_decline`	\(\tau\)	Exponential decline timescale of the early-time component, after peak. \(\log_{10}(\tau/\mathrm{d}) \sim \mathcal{N}(1.8, 0.2^2)\).
`plateau_temperature`	\(T_\mathrm{p}\)	Blackbody temperature of the late-time disk plateau. \(\log_{10}(T_\mathrm{p}/\mathrm{K}) \sim \mathcal{N}(4.0, 0.3^2)\).
`plateau_amplitude`	\(L_\mathrm{p}\)	Plateau bolometric luminosity at \(t_\mathrm{peak}\), \(L_\mathrm{p} = L_\mathrm{bol}^\mathrm{plat}(t_\mathrm{peak})\). \(\log_{10}(L_\mathrm{p}/\mathrm{erg\,s^{-1}}) \sim \mathcal{N}(41.5, 0.2^2)\).
`plateau_timescale`	\(\tau_\mathrm{p}\)	Timescale over which the plateau softens into its power-law decline. \(\log_{10}(\tau_\mathrm{p}/\mathrm{d}) \sim \mathcal{N}(2.3, 0.3^2)\).
`plateau_decline`	\(\alpha_\mathrm{p}\)	Late-time power-law decline index, \(L \propto t^{-\alpha_\mathrm{p}}\) for \(t - t_\mathrm{peak} \gg \tau_\mathrm{p}\). \(\alpha_\mathrm{p} \sim \mathrm{Uniform}(0, 2)\), a wide prior bracketing the magnetized-disk prediction \(\alpha_\mathrm{p}=5/6\) of Alush and Stone[2].

References

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.
`unpack_params_from_arrays`(*parameters)	Convert an ordered sequence of parameter values back into a dict.
`values`()