Create TSmap_freeNormalization_3ML.ipynb

[PbU-Jason]

put my first version of TS map on intro_cosipy

[israelmcmc]

Hey @PbU-Jason ! Thanks for working on this, and sorry it took me so long to look at it.

It looks great! I didn't see any issue with the calculation.

Next week, we can work on:

• Moving this from this notebook to the cosipy library
• Use a healpix map instead of a rectangular RA/Dec grid

Longer term, we can:

• Move the TS map calculation to 3ML, so it works for any instruments, and we can do a joint localization
• Free the index
• Use a multi-resolution map, so we can sample the TS more finely around the best location.

[israelmcmc]

I'm just going to wait a little bit to merge this, I didn't have time to actually run it today. But this is what we'll be doing next week anyway!

[hiyoneda]

Just a minor comment. In my environment (Apple M1 Max), I got an error in the 12th cell. When I changed spectrum.K.value, then it disappeared.

# Set the normalization to 0, that is, background-only null-hypothesis
#spectrum.K.value = spectrum.K.min_value # min_value is a very small value, approx 0. However, it can't be exactly 0 for some internal numerical reason
spectrum.K.value = 1e-12 # min_value is a very small value, approx 0. However, it can't be exactly 0 for some internal numerical reason

# Maximum likelihood
like.fit(quiet=True)

# Compute the likelihood
log_like0 = cosi.get_log_like()

[GallegoSav]

Before merging you need to change after the fit :

# Set the normalization to 0, that is, background-only null-hypothesis
spectrum.K.value = spectrum.K.min_value # min_value is a very small value, approx 0. However, it can't be exactly 0 for some internal numerical reason
# 10⁻30 is to low
spectrum.K.value = 1e-10

# Maximum likelihood
like.fit(quiet=True)

# Compute the likelihood
log_like0 = cosi.get_log_like()

spectrum.K.value need to be 1e-10 otherwise you will have a migrad error

[GallegoSav]

In order to remove the gammapy dependance, you need to remove from gammapy.modeling.models import PowerLawSpectralModel in the cell 1 and

# Here we translate to gammapy. We will make the detector response
            # accept 3ML models and there will be no need for this
            if not isinstance(spectrum, Powerlaw):
               raise RuntimeError("Only PowerLaw supported for now")

            spectrum = PowerLawSpectralModel(
                            index = -spectrum.index.value,
                            amplitude = spectrum.K.value * spectrum.K.unit,
                            reference = spectrum.piv.value * spectrum.piv.unit)
            

in cell 2

[PbU-Jason]

Received! Thanks for helping me find some bugs~
I will set spectrum.K.value to be 1e-10, and also remove the gammapy dependance.