This page is a tutorial for the (GammaBayes)[https://github.com/lpin0002/GammaBayes] and the original notebook can be found within the docs/tutorials folder within the GitHub repository.

HESSCatalogueSources_Prior

The HESSCatalogueSources_Prior class instantiates a conventional astrophysical model for known sources within the HESS catalogue within the defined binning geometry.

It is a sub-class of the SourceFluxDiscreteLogPrior hence requires a pointing direction, observation time and IRFs to construct the exposure.

We’ll construct our binning geometries to be from 0.1 to 100 TeV and -3 to 3 degrees in galactic longitude and latitude.

from gammabayes import GammaBinning
from gammabayes.priors import HESSCatalogueSources_Prior, FermiGaggeroDiffusePrior
from gammabayes.likelihoods.irfs import IRF_LogLikelihood


import numpy as np
from astropy import units as u

pointing_direction = np.array([0., 0.])*u.deg
observation_time = 5*u.hr

true_binning_geometry = GammaBinning(energy_axis=np.logspace(-1, 2, 181)*u.TeV,
                                        lon_axis=np.linspace(-3, 3, 121)*u.deg,
                                        lat_axis=np.linspace(-3, 3, 120)*u.deg)
recon_binning_geometry = GammaBinning(energy_axis=np.logspace(-1, 2, 121)*u.TeV,
                                        lon_axis=np.linspace(-3, 3, 61)*u.deg,
                                        lat_axis=np.linspace(-3, 3, 60)*u.deg)


irf_loglike = IRF_LogLikelihood(
    pointing_dir=pointing_direction,
    observation_time=observation_time,
    binning_geometry = recon_binning_geometry,
    true_binning_geometry=true_binning_geometry,
)



hess_prior = HESSCatalogueSources_Prior(
    binning_geometry=true_binning_geometry,
    pointing_dir=pointing_direction,
    observation_time=observation_time,
    irf_loglike=irf_loglike
)

/Users/lpin0002/anaconda3/envs/testofwest/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
/Users/lpin0002/anaconda3/envs/testofwest/lib/python3.12/site-packages/astropy/units/quantity.py:666: RuntimeWarning: divide by zero encountered in divide
  result = super().__array_ufunc__(function, method, *arrays, **kwargs)
/Users/lpin0002/anaconda3/envs/testofwest/lib/python3.12/site-packages/gammapy/modeling/models/spatial.py:583: RuntimeWarning: invalid value encountered in multiply
  return u.Quantity(norm * np.exp(exponent).value, "sr-1", copy=False)

hess_prior.peek(norm='log', vmin=1e-1, figsize=(12,3), cmap='afmhot')

(<Figure size 1200x300 with 5 Axes>,
 array([<Axes: xlabel='Energy [TeV]'>,
        <Axes: xlabel='Longitude [deg]', ylabel='Latitude [deg]'>,
        <Axes: xlabel='Energy [TeV]', ylabel='Longitude [deg]'>],
       dtype=object))

FermiGaggeroDiffusePrior

The FermiGaggeroDiffusePrior class instantiates a conventional astrophysical model for diffuse emission around the galactic centre. The morphology follows that of the Pass8 Fermi IEM model and the spectral distribution follows that of Gaggero et al. 2017.

It is a sub-class of the SourceFluxDiscreteLogPrior hence requires a pointing direction, observation time and IRFs to construct the exposure.

We’ll construct our binning geometries to be from 0.1 to 100 TeV and -3 to 3 degrees in galactic longitude and latitude.

from gammabayes import GammaBinning
from gammabayes.priors import HESSCatalogueSources_Prior, FermiGaggeroDiffusePrior
from gammabayes.likelihoods.irfs import IRF_LogLikelihood


import numpy as np
from astropy import units as u

pointing_direction = np.array([0., 0.])*u.deg
observation_time = 5*u.hr

true_binning_geometry = GammaBinning(energy_axis=np.logspace(-1, 2, 181)*u.TeV,
                                        lon_axis=np.linspace(-3, 3, 121)*u.deg,
                                        lat_axis=np.linspace(-3, 3, 120)*u.deg)
recon_binning_geometry = GammaBinning(energy_axis=np.logspace(-1, 2, 121)*u.TeV,
                                        lon_axis=np.linspace(-3, 3, 61)*u.deg,
                                        lat_axis=np.linspace(-3, 3, 60)*u.deg)



irf_loglike = IRF_LogLikelihood(
    pointing_dir=pointing_direction,
    observation_time=observation_time,
    binning_geometry = recon_binning_geometry,
    true_binning_geometry=true_binning_geometry,
)

diffuse_prior = FermiGaggeroDiffusePrior(
    binning_geometry=true_binning_geometry,
    pointing_dir=pointing_direction,
    observation_time=observation_time,
    irf_loglike=irf_loglike
)

/Users/lpin0002/anaconda3/envs/testofwest/lib/python3.12/site-packages/gammabayes/priors/astro_sources/diffuse_emission/fermi_gaggero_diffuse.py:79: RuntimeWarning: divide by zero encountered in log
  fermi_integral_values= logspace_integrator(logy=np.log(fermievaluated.value), x=energy_axis, axis=0)

diffuse_prior.peek(norm='log', vmin=2e3, vmax=2e4, figsize=(12,3), cmap='afmhot')

(<Figure size 1200x300 with 5 Axes>,
 array([<Axes: xlabel='Energy [TeV]'>,
        <Axes: xlabel='Longitude [deg]', ylabel='Latitude [deg]'>,
        <Axes: xlabel='Energy [TeV]', ylabel='Longitude [deg]'>],
       dtype=object))