DarkMatterAnnihilationSpectralModel

class gammapy.astro.darkmatter.DarkMatterAnnihilationSpectralModel

Bases: SpectralModel

Dark matter annihilation spectral model.

The gamma-ray flux is computed as follows:

\[\frac{\mathrm d \phi}{\mathrm d E} = \frac{\langle \sigma\nu \rangle}{4\pi k m^2_{\mathrm{DM}}} \frac{\mathrm d N}{\mathrm dE} \times J(\Delta\Omega)\]

Parameters:

massQuantity

Dark matter mass.

channelstr

Annihilation channel for PrimaryFlux, e.g. “b” for “bbar”. See PrimaryFlux.channel_registry for more.

scalefloat

Scale parameter for model fitting.

jfactorQuantity

Integrated J-Factor needed when PointSpatialModel is used.

z: float

Redshift value.

k: int

Type of dark matter particle (k:2 Majorana, k:4 Dirac).

References

Marco et al. (2011), “PPPC 4 DM ID: a poor particle physicist cookbook for dark matter indirect detection”

Examples

This is how to instantiate a DarkMatterAnnihilationSpectralModel model:

>>> import astropy.units as u
>>> from gammapy.astro.darkmatter import DarkMatterAnnihilationSpectralModel

>>> channel = "b"
>>> massDM = 5000*u.Unit("GeV")
>>> jfactor = 3.41e19 * u.Unit("GeV2 cm-5")
>>> modelDM = DarkMatterAnnihilationSpectralModel(mass=massDM, channel=channel, jfactor=jfactor)  # noqa: E501

Attributes Summary

THERMAL_RELIC_CROSS_SECTION	Thermally averaged annihilation cross-section
default_parameters
scale	A model parameter.
tag

Methods Summary

evaluate(energy, scale)	Evaluate dark matter annihilation model.
from_dict(data)	Create spectral model from a dictionary.
to_dict([full_output])	Convert to dictionary.

Attributes Documentation

THERMAL_RELIC_CROSS_SECTION = <Quantity 3.e-26 cm3 / s>

Thermally averaged annihilation cross-section

default_parameters = <gammapy.modeling.parameter.Parameters object>

scale

A model parameter.

Note that the parameter value has been split into a factor and scale like this:

value = factor x scale

Users should interact with the value, quantity or min and max properties and consider the fact that there is a factor and scale an implementation detail.

That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the factor, factor_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters:

namestr

Name.

valuefloat or Quantity

Value.

scalefloat, optional

Scale (sometimes used in fitting).

unitUnit or str, optional

Unit. Default is “”.

minfloat, str or quantity, optional

Minimum (sometimes used in fitting). If None, set to numpy.nan. Default is None.

maxfloat, str or quantity, optional

Maximum (sometimes used in fitting). Default is numpy.nan.

frozenbool, optional

Frozen (used in fitting). Default is False.

errorfloat, optional

Parameter error. Default is 0.

scan_minfloat, optional

Minimum value for the parameter scan. Overwrites scan_n_sigma. Default is None.

scan_maxfloat, optional

Maximum value for the parameter scan. Overwrites scan_n_sigma. Default is None.

scan_n_values: int, optional

Number of values to be used for the parameter scan. Default is 11.

scan_n_sigmaint, optional

Number of sigmas to scan. Default is 2.

scan_values: `numpy.array`, optional

Scan values. Overwrites all the scan keywords before. Default is None.

scale_method{‘scale10’, ‘factor1’, None}, optional

Method used to set factor and scale. Default is “scale10”.

interp{“lin”, “sqrt”, “log”}, optional

Parameter scaling to use for the scan. Default is “lin”.

priorPrior, optional

Prior set on the parameter. Default is None.

tag = ['DarkMatterAnnihilationSpectralModel', 'dm-annihilation']

Methods Documentation

evaluate(energy, scale)

Evaluate dark matter annihilation model.

classmethod from_dict(data)

Create spectral model from a dictionary.

Parameters:

datadict

Dictionary with model data.

Returns:

modelDarkMatterAnnihilationSpectralModel

Dark matter annihilation spectral model.

to_dict(full_output=False)

Convert to dictionary.

__init__(mass, channel, scale=<Quantity 1.>, jfactor=1, z=0, k=2)

classmethod __new__(*args, **kwargs)