Add method to compute energy-weighted 2D PSF kernel #1837
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registerrier
requested changes
Sep 28, 2018
gammapy/cube/psf_kernel.py
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| Parameters | ||
| ---------- | ||
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| exposure : Pre-computed exposure map |
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Technically one should not use an exposure map to average the PSF but simply a vector of exposures at the typical tested position. So here you could pass either an array of exposures for the same true energies.
| exposure = Map.from_geom(geom) | ||
| exposure.data = np.ones(exposure.data.shape) | ||
| sigma = 0.5 * u.deg | ||
| kernel = PSFKernel.from_gauss(geom, sigma) |
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Here you should probably have two different sigmas and average over bins such that the expected statistic is constant, e.g. for constant exposure and E^-2 spectrum 100, 150 and 230 GeV
cdeil
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This method should weigh the PSF with the exposure and the spectrum before averaging.
I have put in a very rudimentary test, because I am not very sure about the "expected" values after averaging.