implement  psf weighting
"cell_type": "markdown",
"metadata": {},
"source": [
- "For a typical 2D analysis, using an energy dispersion usually does not make sense. A PSF map can be made as in the regular 3D case."
+ "For a typical 2D analysis, using an energy dispersion usually does not make sense. A PSF map can be made as in the regular 3D case, taking care to weight it properly with the spectrum."
]
},
{
"src_pos = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n",
"table_psf = make_mean_psf(observations, src_pos)\n",
"\n",
+ "table_psf_2d = table_psf.table_psf_in_energy_band((emin, emax), spectrum=spectrum)\n",
+ "\n",
"# PSF kernel used for the model convolution\n",
- "psf_kernel = PSFKernel.from_table_psf(table_psf, geom2d, max_radius=\"0.3 deg\")"
+ "psf_kernel = PSFKernel.from_table_psf(table_psf_2d, geom2d, max_radius=\"0.3 deg\")"
]
},
{
"1. Plot residual maps as done in the `analysis_3d` notebook\n",
"2. Iteratively add and fit sources as explained in `image_fitting_with_sherpa` notebook\n"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
