X-ray Models

X-ray Models#

X-ray Emission#

The X-ray fitting module implements two simple power law models for XRB populations and AGN, as well as a version of the QSOSED model of Kubota & Done (2018).

One noteworthy point about the StellarPlaw and AGNPlaw models is that they both rely on knowledge of the corresponding UV-IR models to set their normalization. You’ll thus see that the StellarPlaw get_* functions all require a stellar model and its parameters as input, while their equivalents for the AGNPlaw model require the AGN model and its parameters.

class lightning.xray.StellarPlaw#

Bases: XrayPlawExpcut

Simple model for stellar X-ray emission.

Inclues a stellar-age parameterization of the luminosity, such that the model normalization is a function of the SFH. The high energy cutoff is fixed, but the photon index can vary.

The luminosity is determined from the SFH model based on the empirical Lx/M - stellar age relationship from Gilbertson+(2022).

Parameters:

filter_labelslist, str: List of filter labels.
arfdict or astropy.table.Table or numpy structured array: A structure defining the anciliary response function (ARF) of your X-ray observations. The structure must have three keys, ‘ENERG_LO’, ‘ENERG_HI’, and ‘SPECRESP’, which given the energy bins and binned spectral response respectively. Only used if xray_mode=’counts’`.
exposurefloat or np.ndarray (Nfilters): A scalar or array giving the exposure time of the X-ray observations. If an array, it should have the same length as filter_labels, with all non-X-ray bands having their exposure time set to 0. Note that you almost certainly don’t need to give exposure time as an array, since the energy dependence of the effective area is explicitly given by the ARF. Only used if xray_mode='counts'.
redshiftfloat: Redshift of the model. If set, lum_dist is ignored.
lum_distfloat: Luminosity distance to the model. If not set, this will be calculated from the redshift and cosmology. (Default: None)
cosmologyastropy.cosmology.FlatLambdaCDM: The cosmology to assume. Lightning defaults to a flat cosmology with h=0.7 and Om0=0.3.
path_to_modelsstr: Path to lightning models. Not actually used in normal circumstances.
path_to_filtersstr: Path to lightning filters. Not actually used in normal circumstances.
wave_gridtuple (3,), or np.ndarray, (Nwave,), float32, optional: Either a tuple of (lo, hi, Nwave) specifying a log-spaced rest-frame wavelength grid, or an array giving the wavelengths directly. At high redshift this should be constructed carefully to ensure that your bands are covered. (Default: (1e-6, 1e-1, 200))

Methods

`get_model_countrate`(params, stellar_model, ...)	Construct the bandpass-convolved SED in count-rate.
`get_model_countrate_hires`(params, ...[, exptau])	Construct the high-resolution countrate-density spectrum.
`get_model_counts`(params, stellar_model, ...)	Construct the bandpass-convolved SED in counts.
`get_model_lnu`(params, stellar_model, ...[, ...])	Construct the bandpass-convolved SED in Lnu.
`get_model_lnu_hires`(params, stellar_model, ...)	Construct the high-resolution spectrum in Lnu.
`print_params`([verbose])	If verbose, print a nicely formatted table of the models, their parameters, and the description of the parameters.

References

Gilbertson et al. (2022)

get_model_countrate(params, stellar_model, stellar_params, sfh, sfh_params, exptau=None)#

Construct the bandpass-convolved SED in count-rate.

This function takes in the stellar and SFH models in order to use the stellar-age parametrization of Lx / M from Gilbertson et al. (2022).

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
stellar_modellightning.stellar model: The stellar model.
stellar_paramsnp.ndarray(Nmodels, Nparams_st) or np.ndarray(Nparams_st): The stellar model parameters (i.e. metallicity and possible logU).
sfhlightning.sfh.PiecewiseConstSFH or lightning.sfh.FunctionalSFH: The star formation history model.
sfh_paramsnp.ndarray(Nmodels, Nparams_sfh) or np.ndarray(Nparams_sfh): The parameters for the SFH.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

Notes

The absorption free count-rate is not returned by default. It can be accessed by setting exptau to None.

get_model_countrate_hires(params, stellar_model, stellar_params, sfh, sfh_params, exptau=None)#

Construct the high-resolution countrate-density spectrum.

This function takes in the stellar and SFH models in order to use the stellar-age parametrization of Lx / M from Gilbertson et al. (2022).

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
stellar_modellightning.stellar model: The stellar model.
stellar_paramsnp.ndarray(Nmodels, Nparams_st) or np.ndarray(Nparams_st): The stellar model parameters (i.e. metallicity and possible logU).
sfhlightning.sfh.PiecewiseConstSFH or lightning.sfh.FunctionalSFH: The star formation history model.
sfh_paramsnp.ndarray(Nmodels, Nparams_sfh) or np.ndarray(Nparams_sfh): The parameters for the SFH.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

Notes

The absorption free count-rate spectrum is not returned by default. It can be accessed by setting exptau to None.

get_model_counts(params, stellar_model, stellar_params, sfh, sfh_params, exptau=None)#

Construct the bandpass-convolved SED in counts.

This function takes in the stellar and SFH models in order to use the stellar-age parametrization of Lx / M from Gilbertson et al. (2022).

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
stellar_modellightning.stellar model: The stellar model.
stellar_paramsnp.ndarray(Nmodels, Nparams_st) or np.ndarray(Nparams_st): The stellar model parameters (i.e. metallicity and possible logU).
sfhlightning.sfh.PiecewiseConstSFH or lightning.sfh.FunctionalSFH: The star formation history model.
sfh_paramsnp.ndarray(Nmodels, Nparams_sfh) or np.ndarray(Nparams_sfh): The parameters for the SFH.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

Notes

The absorption free counts are not returned by default. They can be accessed by setting exptau to None.

get_model_lnu(params, stellar_model, stellar_params, sfh, sfh_params, exptau=None)#

Construct the bandpass-convolved SED in Lnu.

This function takes in the stellar and SFH models in order to use the stellar-age parametrization of Lx / M from Gilbertson et al. (2022).

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
stellar_modellightning.stellar model: The stellar model.
stellar_paramsnp.ndarray(Nmodels, Nparams_st) or np.ndarray(Nparams_st): The stellar model parameters (i.e. metallicity and possible logU).
sfhlightning.sfh.PiecewiseConstSFH or lightning.sfh.FunctionalSFH: The star formation history model.
sfh_paramsnp.ndarray(Nmodels, Nparams_sfh) or np.ndarray(Nparams_sfh): The parameters for the SFH.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

get_model_lnu_hires(params, stellar_model, stellar_params, sfh, sfh_params, exptau=None)#

Construct the high-resolution spectrum in Lnu.

This function takes in the stellar and SFH models in order to use the stellar-age parametrization of Lx / M from Gilbertson et al. (2022).

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
stellar_modellightning.stellar model: The stellar model.
stellar_paramsnp.ndarray(Nmodels, Nparams_st) or np.ndarray(Nparams_st): The stellar model parameters (i.e. metallicity and possible logU).
sfhlightning.sfh.PiecewiseConstSFH or lightning.sfh.FunctionalSFH: The star formation history model.
sfh_paramsnp.ndarray(Nmodels, Nparams_sfh) or np.ndarray(Nparams_sfh): The parameters for the SFH.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

class lightning.xray.AGNPlaw#

Bases: XrayPlawExpcut

Simple model for AGN X-ray emission.

Uses the Lusso and Risaliti (2017) relationship to connect the intrinsic accretion disk luminosity at 2500 Angstroms to the X-ray luminosity at 2 keV. The high energy cutoff is fixed, but the photon index can vary.

The model includes a parameter representing the deviation from the LR17 relationship. Similar parameters are sometimes called ‘x-ray weakness’ - an underluminous X-ray spectrum compared to the prediction may be a result of mass loading in the corona providing an alternate source of cooling.

Parameters:

filter_labelslist, str: List of filter labels.
arfdict or astropy.table.Table or numpy structured array: A structure defining the anciliary response function (ARF) of your X-ray observations. The structure must have three keys, ‘ENERG_LO’, ‘ENERG_HI’, and ‘SPECRESP’, which given the energy bins and binned spectral response respectively. Only used if xray_mode=’counts’`.
exposurefloat or np.ndarray (Nfilters): A scalar or array giving the exposure time of the X-ray observations. If an array, it should have the same length as filter_labels, with all non-X-ray bands having their exposure time set to 0. Note that you almost certainly don’t need to give exposure time as an array, since the energy dependence of the effective area is explicitly given by the ARF. Only used if xray_mode='counts'.
redshiftfloat: Redshift of the model. If set, lum_dist is ignored.
lum_distfloat: Luminosity distance to the model. If not set, this will be calculated from the redshift and cosmology. (Default: None)
cosmologyastropy.cosmology.FlatLambdaCDM: The cosmology to assume. Lightning defaults to a flat cosmology with h=0.7 and Om0=0.3.
wave_gridtuple (3,), or np.ndarray, (Nwave,), float32, optional: Either a tuple of (lo, hi, Nwave) specifying a log-spaced rest-frame wavelength grid, or an array giving the wavelengths directly. At high redshift this should be constructed carefully to ensure that your bands are covered. (Default: (1e-6, 1e-1, 200))

Methods

`get_model_countrate`(params, agn_model, ...)	Construct the bandpass-convolved SED in count-rate.
`get_model_countrate_hires`(params, agn_model, ...)	Construct the high-resolution spectrum in count-rate density.
`get_model_counts`(params, agn_model, agn_params)	Construct the high-resolution spectrum in Lnu.
`get_model_lnu`(params, agn_model, agn_params)	Construct the bandpass-convolved SED in Lnu.
`get_model_lnu_hires`(params, agn_model, ...)	Construct the high-resolution spectrum in Lnu.
`print_params`([verbose])	If verbose, print a nicely formatted table of the models, their parameters, and the description of the parameters.

References

Lusso and Risaliti (2018)

get_model_countrate(params, agn_model, agn_params, exptau=None)#

Construct the bandpass-convolved SED in count-rate.

This function takes in the agn model to normalize the X-ray spectrum with the Lusso & Risaliti (2017) L2keV - L2500 relationship.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
agn_modellightning.agn.AGNModel: The AGN model.
agn_paramsnp.ndarray(Nmodels, Nparams_agn) or np.ndarray(Nparams_agn): The parameters for the AGN model.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

Notes

The absorption free count-rate is not returned by default. It can be accessed by setting exptau to None.

get_model_countrate_hires(params, agn_model, agn_params, exptau=None)#

Construct the high-resolution spectrum in count-rate density.

This function takes in the agn model to normalize the X-ray spectrum with the Lusso & Risaliti (2017) L2keV - L2500 relationship.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
agn_modellightning.agn.AGNModel: The AGN model.
agn_paramsnp.ndarray(Nmodels, Nparams_agn) or np.ndarray(Nparams_agn): The parameters for the AGN model.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

Notes

The absorption free count-rate spectrum is not returned by default. It can be accessed by setting exptau to None.

get_model_counts(params, agn_model, agn_params, exptau=None)#

Construct the high-resolution spectrum in Lnu.

This function takes in the agn model to normalize the X-ray spectrum with the Lusso & Risaliti (2017) L2keV - L2500 relationship.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
agn_modellightning.agn.AGNModel: The AGN model.
agn_paramsnp.ndarray(Nmodels, Nparams_agn) or np.ndarray(Nparams_agn): The parameters for the AGN model.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

Notes

The absorption free counts are not returned by default. They can be accessed by setting exptau to None.

get_model_lnu(params, agn_model, agn_params, exptau=None)#

Construct the bandpass-convolved SED in Lnu.

This function takes in the agn model to normalize the X-ray spectrum with the Lusso & Risaliti (2017) L2keV - L2500 relationship.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
agn_modellightning.agn.AGNModel: The AGN model.
agn_paramsnp.ndarray(Nmodels, Nparams_agn) or np.ndarray(Nparams_agn): The parameters for the AGN model.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

get_model_lnu_hires(params, agn_model, agn_params, exptau=None)#

Construct the high-resolution spectrum in Lnu.

This function takes in the agn model to normalize the X-ray spectrum with the Lusso & Risaliti (2017) L2keV - L2500 relationship.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
agn_modellightning.agn.AGNModel: The AGN model.
agn_paramsnp.ndarray(Nmodels, Nparams_agn) or np.ndarray(Nparams_agn): The parameters for the AGN model.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

The QSOSED model, however, doesn’t need to know about the UV-IR AGN model, because the opposite is true: the UV-IR AGN component is normalized by the QSOSED model, allowing the black hole mass and Eddington ratio to set the overall luminosity of the entire AGN model.

class lightning.xray.Qsosed#

Bases: XrayEmissionModel

Physically motivated quasar model for X-ray emission.

Parametrized by the black hole mass and Eddington ratio (here called mdot). See Kubota & Done (2018) for details.

The model is available in XSpec, and the implementation here was generated using Sherpa.

Parameters:

filter_labelslist, str: List of filter labels.
arfdict or astropy.table.Table or numpy structured array: A structure defining the anciliary response function (ARF) of your X-ray observations. The structure must have three keys, ‘ENERG_LO’, ‘ENERG_HI’, and ‘SPECRESP’, which given the energy bins and binned spectral response respectively. Only used if xray_mode=’counts’`.
exposurefloat or np.ndarray (Nfilters): A scalar or array giving the exposure time of the X-ray observations. If an array, it should have the same length as filter_labels, with all non-X-ray bands having their exposure time set to 0. Note that you almost certainly don’t need to give exposure time as an array, since the energy dependence of the effective area is explicitly given by the ARF. Only used if xray_mode='counts'.
redshiftfloat: Redshift of the model. If set, lum_dist is ignored.
lum_distfloat: Luminosity distance to the model. If not set, this will be calculated from the redshift and cosmology. (Default: None)
cosmologyastropy.cosmology.FlatLambdaCDM: The cosmology to assume. Lightning defaults to a flat cosmology with h=0.7 and Om0=0.3.
wave_gridtuple (3,), or np.ndarray, (Nwave,), float32, optional: Either a tuple of (lo, hi, Nwave) specifying a log-spaced rest-frame wavelength grid, or an array giving the wavelengths directly. At high redshift this should be constructed carefully to ensure that your bands are covered. (Default: (1e-6, 1e-1, 200))

Methods

`get_model_L2500`(params)	Calculate the intrinsic L2500
`get_model_countrate`(params[, exptau])	Construct the bandpass-convolved SED in count-rate.
`get_model_countrate_hires`(params[, exptau])	Construct the high-resolution spectrum in count-rate density.
`get_model_counts`(params[, exptau])	Construct the high-resolution spectrum in Lnu.
`get_model_lnu`(params[, exptau])	Construct the bandpass-convolved SED in Lnu.
`get_model_lnu_hires`(params[, exptau])	Construct the high-resolution spectrum in Lnu.
`print_params`([verbose])	If verbose, print a nicely formatted table of the models, their parameters, and the description of the parameters.

References

Kubota and Done (2018)

get_model_L2500(params)#

Calculate the intrinsic L2500

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.

get_model_countrate(params, exptau=None)#

Construct the bandpass-convolved SED in count-rate.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

Notes

The absorption free count-rate is not returned by default. It can be accessed by setting exptau to None.

get_model_countrate_hires(params, exptau=None)#

Construct the high-resolution spectrum in count-rate density.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

Notes

The absorption free count-rate spectrum is not returned by default. It can be accessed by setting exptau to None.

get_model_counts(params, exptau=None)#

Construct the high-resolution spectrum in Lnu.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

Notes

The absorption free counts are not returned by default. They can be accessed by setting exptau to None.

get_model_lnu(params, exptau=None)#

Construct the bandpass-convolved SED in Lnu.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

get_model_lnu_hires(params, exptau=None)#

Construct the high-resolution spectrum in Lnu.

Parameters:

paramsnp.ndarray(Nmodels, Nparams) or np.ndarray(Nparams): An array of model parameters. For purposes of vectorization this can be a 2D array, where the first dimension cycles over different sets of parameters.
exptaunp.ndarray(Nmodels, Nwave) or np.ndarray(Nwave): The e(-tau) absorption curve.

The emission models above extend the XrayPlaw and XrayEmissionModel classes documented at the bottom of this page for the sake of completeness.

X-ray Absorption#

Two absorption models are implemented: the Tubingen-Boulder absorption model (tbabs) which includes more atomic physics and extends to the UV, and the photoelectric absorption model from XSpec (phabs) which covers only the X-rays.

class lightning.xray.Tbabs#

Bases: TabulatedAtten

Tubingen-Boulder absorption model.

Includes cross sections from gas phase ISM, grains, and molecular hydrogen. Atomic abundances are fixed to the default.

Parameters:

wavenp.ndarray, (Nwave,), float: Rest frame wavelength grid to evaluate the model on.
path_to_modelsstr: Path to lightning models. Not actually used in normal circumstances.

Methods

`evaluate`(params)	Evaluate the absorption as a function of wavelength for the given parameters.
`print_params`([verbose])	If verbose, print a nicely formatted table of the models, their parameters, and the description of the parameters.

References

evaluate(params)#

Evaluate the absorption as a function of wavelength for the given parameters.

Parameters:

paramsnp.ndarray, (Nmodels, 1) or (1,): Values for NH.

Returns:

expminustau(Nmodels, Nwave)

class lightning.xray.Phabs#

Bases: TabulatedAtten

Photo-electric absorption model.

Abundances are fixed to the default.

Parameters:

wavenp.ndarray, (Nwave,), float: Rest frame wavelength grid to evaluate the model on.
path_to_modelsstr: Path to lightning models. Not actually used in normal circumstances.

Methods

`evaluate`(params)	Evaluate the absorption as a function of wavelength for the given parameters.
`print_params`([verbose])	If verbose, print a nicely formatted table of the models, their parameters, and the description of the parameters.

References

https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/node264.html

evaluate(params)#

Evaluate the absorption as a function of wavelength for the given parameters.

Parameters:

paramsnp.ndarray, (Nmodels, 1) or (1,): Values for NH.

Returns:

expminustau(Nmodels, Nwave)

X-ray Base Classes#

class lightning.xray.XrayPlawExpcut#

Bases: XrayEmissionModel

Power law emission model with an exponential cutoff at high energy.

Methods

`get_model_countrate`(params[, exptau])	Produce the mean model countrate density in the bandpass.
`get_model_countrate_hires`(params[, exptau])	Produce the high-resolution model countrate density in counts s-1 Hz-1.
`get_model_counts`(params[, exptau])	Produce the model counts in the bandpass.
`get_model_lnu`(params[, exptau])	Overwrite this method to provide the actual evaluation of the model as observed in the given filters.
`get_model_lnu_hires`(params[, exptau])	Overwrite this method to provide the actual evaluation of the high-res spectral model.
`print_params`([verbose])	If verbose, print a nicely formatted table of the models, their parameters, and the description of the parameters.

get_model_countrate(params, exptau=None)#: Produce the mean model countrate density in the bandpass.

get_model_countrate_hires(params, exptau=None)#: Produce the high-resolution model countrate density in counts s-1 Hz-1. Note that these could probably be implemented here; the only thing that’s specific to each individual model is the high-resolution Lnu spectrum.

get_model_counts(params, exptau=None)#: Produce the model counts in the bandpass.

get_model_lnu(params, exptau=None)#: Overwrite this method to provide the actual evaluation of the model as observed in the given filters.

get_model_lnu_hires(params, exptau=None)#: Overwrite this method to provide the actual evaluation of the high-res spectral model.

class lightning.xray.base.XrayEmissionModel#

Bases: BaseEmissionModel

Base class for X-ray emission models.

Methods

`get_model_countrate`(params)	Produce the mean model countrate density in the bandpass.
`get_model_countrate_hires`(params)	Produce the high-resolution model countrate density in counts s-1 Hz-1.
`get_model_counts`(params)	Produce the model counts in the bandpass.
`get_model_lnu`(params)	Overwrite this method to provide the actual evaluation of the model as observed in the given filters.
`get_model_lnu_hires`(params)	Overwrite this method to provide the actual evaluation of the high-res spectral model.
`print_params`([verbose])	If verbose, print a nicely formatted table of the models, their parameters, and the description of the parameters.

__init__(filter_labels, arf, exposure, redshift, lum_dist=None, cosmology=None, **kwargs)#: Generic initialization. Actual model-building should be handled by implementing the construct_model and construct_model_grid methods.

get_model_countrate(params)#: Produce the mean model countrate density in the bandpass.

get_model_countrate_hires(params)#: Produce the high-resolution model countrate density in counts s-1 Hz-1. Note that these could probably be implemented here; the only thing that’s specific to each individual model is the high-resolution Lnu spectrum.

get_model_counts(params)#: Produce the model counts in the bandpass.

X-ray Models

Contents

X-ray Models#

X-ray Emission#

X-ray Absorption#

X-ray Base Classes#