Flux Profile Estimation#

Learn how to estimate flux profiles on a Fermi-LAT dataset.

Prerequisites#

Knowledge of 3D data reduction and datasets used in Gammapy, see for instance the first analysis tutorial.

Context#

A useful tool to study and compare the saptial distribution of flux in images and data cubes is the measurement of flxu profiles. Flux profiles can show spatial correlations of gamma-ray data with e.g. gas maps or other type of gamma-ray data. Most commonly flux profiles are measured along some preferred coordinate axis, either radially distance from a source of interest, along longitude and latitude coordinate axes or along the path defined by two spatial coordinates.

Proposed Approach#

Flux profile estimation essentially works by estimating flux points for a set of predefined spatially connected regions. For radial flux profiles the shape of the regions are annuli with a common center, for linear profiles it’s typically a rectangular shape.

We will work on a pre-computed MapDataset of Fermi-LAT data, use SkyRegion to define the structure of the bins of the flux profile and run the actually profile extraction using the FluxProfileEstimator

import numpy as np
from astropy import units as u
from astropy.coordinates import SkyCoord

# %matplotlib inline
import matplotlib.pyplot as plt

Setup#

from IPython.display import display
from gammapy.datasets import MapDataset
from gammapy.estimators import FluxPoints, FluxProfileEstimator
from gammapy.maps import RegionGeom
from gammapy.modeling.models import PowerLawSpectralModel

Check setup#

from gammapy.utils.check import check_tutorials_setup
from gammapy.utils.regions import (
    make_concentric_annulus_sky_regions,
    make_orthogonal_rectangle_sky_regions,
)

check_tutorials_setup()
System:

        python_executable      : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/bin/python
        python_version         : 3.9.16
        machine                : x86_64
        system                 : Linux


Gammapy package:

        version                : 1.0.1
        path                   : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy


Other packages:

        numpy                  : 1.24.2
        scipy                  : 1.10.1
        astropy                : 5.2.1
        regions                : 0.7
        click                  : 8.1.3
        yaml                   : 6.0
        IPython                : 8.11.0
        jupyterlab             : not installed
        matplotlib             : 3.7.1
        pandas                 : not installed
        healpy                 : 1.16.2
        iminuit                : 2.21.0
        sherpa                 : 4.15.0
        naima                  : 0.10.0
        emcee                  : 3.1.4
        corner                 : 2.2.1


Gammapy environment variables:

        GAMMAPY_DATA           : /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/1.0.1

Read and Introduce Data#

dataset = MapDataset.read(
    "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc.fits.gz", name="fermi-dataset"
)

This is what the counts image we will work with looks like:

flux profiles
<WCSAxes: >

There are 400x200 pixels in the dataset and 11 energy bins between 10 GeV and 2 TeV:

WcsNDMap

        geom  : WcsGeom
        axes  : ['lon', 'lat', 'energy']
        shape : (400, 200, 11)
        ndim  : 3
        unit  :
        dtype : >i4

Profile Estimation#

Configuration#

We start by defining a list of spatially connected regions along the galactic longitude axis. For this there is a helper function make_orthogonal_rectangle_sky_regions. The individual region bins for the profile have a height of 3 deg and in total there are 31 bins. The starts from lon = 10 deg tand goes to lon = 350 deg. In addition we have to specify the wcs to take into account possible projections effects on the region definition:

regions = make_orthogonal_rectangle_sky_regions(
    start_pos=SkyCoord("10d", "0d", frame="galactic"),
    end_pos=SkyCoord("350d", "0d", frame="galactic"),
    wcs=counts_image.geom.wcs,
    height="3 deg",
    nbin=51,
)

We can use the RegionGeom object to illustrate the regions on top of the counts image:

plt.figure()
geom = RegionGeom.create(region=regions)
ax = counts_image.smooth("0.1 deg").plot(stretch="sqrt")
geom.plot_region(ax=ax, color="w")
flux profiles
/home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/regions/shapes/rectangle.py:208: UserWarning: Setting the 'color' property will override the edgecolor or facecolor properties.
  return Rectangle(xy=xy, width=width, height=height,

<WCSAxes: >

Next we create the FluxProfileEstimator. For the estimation of the flux profile we assume a spectral model with a power-law shape and an index of 2.3

flux_profile_estimator = FluxProfileEstimator(
    regions=regions,
    spectrum=PowerLawSpectralModel(index=2.3),
    energy_edges=[10, 2000] * u.GeV,
    selection_optional=["ul"],
)

We can see the full configuration by printing the estimator object:

FluxProfileEstimator
--------------------

  energy_edges           : [  10. 2000.] GeV
  fit                    : <gammapy.modeling.fit.Fit object at 0x7f31817ccca0>
  n_sigma                : 1
  n_sigma_ul             : 2
  norm_max               : 5
  norm_min               : 0.2
  norm_n_values          : 11
  norm_values            : None
  null_value             : 0
  reoptimize             : False
  selection_optional     : ['ul']
  source                 : 0
  spectrum               : PowerLawSpectralModel
  sum_over_energy_groups : False

Run Estimation#

Now we can run the profile estimation and explore the results:

FluxPoints
----------

  geom                   : RegionGeom
  axes                   : ['lon', 'lat', 'energy', 'projected-distance']
  shape                  : (1, 1, 1, 51)
  quantities             : ['norm', 'norm_err', 'norm_ul', 'ts', 'npred', 'npred_excess', 'stat', 'counts', 'success']
  ref. model             : pl
  n_sigma                : 1
  n_sigma_ul             : 2
  sqrt_ts_threshold_ul   : 2
  sed type init          : likelihood

We can see the flux profile is represented by a FluxPoints object with a projected-distance axis, which defines the main axis the flux profile is measured along. The lon and lat axes can be ignored.

Plotting Results#

Let us directly plot the result using plot:

plt.figure()
ax = profile.plot(sed_type="dnde")
ax.set_yscale("linear")
flux profiles

Based on the spectral model we specified above we can also plot in any other sed type, e.g. energy flux and define a different threshold when to plot upper limits:

flux profiles

We can also plot any other quantity of interest, that is defined on the FluxPoints result object. E.g. the predicted total counts, background counts and excess counts:

quantities = ["npred", "npred_excess", "npred_background"]

fig, ax = plt.subplots()

for quantity in quantities:
    profile[quantity].plot(ax=ax, label=quantity.title())

ax.set_ylabel("Counts")
flux profiles
Text(38.347222222222214, 0.5, 'Counts')

Serialisation and I/O#

The profile can be serialised using write, given a specific format:

profile.write(
    filename="flux_profile_fermi.fits",
    format="profile",
    overwrite=True,
    sed_type="dnde",
)

profile_new = FluxPoints.read(filename="flux_profile_fermi.fits", format="profile")

fig = plt.figure()
ax = profile_new.plot()
ax.set_yscale("linear")
flux profiles

The profile can be serialised to a Table object using:

table = profile.to_table(format="profile", formatted=True)
display(table)
       x_min                x_max              x_ref        ... counts success
        deg                  deg                deg         ...
-------------------- ------------------- ------------------ ... ------ -------
-0.19607843137254918 0.19607843137254918                0.0 ...    0.0   False
 0.19607843137254918  0.5882352941176466 0.3921568627450979 ...    0.0   False
  0.5882352941176466  0.9803921568627441 0.7843137254901953 ...  163.0    True
  0.9803921568627441  1.3725490196078436  1.176470588235294 ...  448.0    True
  1.3725490196078436  1.7647058823529418 1.5686274509803928 ...  599.0    True
  1.7647058823529418  2.1568627450980395 1.9607843137254908 ...  354.0    True
  2.1568627450980395   2.549019607843138 2.3529411764705888 ...  367.0    True
   2.549019607843138   2.941176470588236  2.745098039215687 ...  339.0    True
   2.941176470588236  3.3333333333333344  3.137254901960785 ...  531.0    True
  3.3333333333333344   3.725490196078432  3.529411764705883 ...  458.0    True
                 ...                 ...                ... ...    ...     ...
  15.882352941176464  16.274509803921596  16.07843137254903 ...  321.0    True
  16.274509803921596  16.666666666666696 16.470588235294144 ...  421.0    True
  16.666666666666696  17.058823529411775 16.862745098039234 ...  431.0    True
  17.058823529411775    17.4509803921569 17.254901960784338 ...  374.0    True
    17.4509803921569  17.843137254902004 17.647058823529452 ...  370.0    True
  17.843137254902004   18.23529411764708  18.03921568627454 ...  410.0    True
   18.23529411764708  18.627450980392158  18.43137254901962 ...  336.0    True
  18.627450980392158   19.01960784313726  18.82352941176471 ...  172.0    True
   19.01960784313726   19.41176470588239 19.215686274509824 ...    0.0   False
   19.41176470588239    19.8039215686275 19.607843137254946 ...    0.0   False
Length = 51 rows

No we can also estimate a radial profile starting from the Galactic center:

regions = make_concentric_annulus_sky_regions(
    center=SkyCoord("0d", "0d", frame="galactic"),
    radius_max="1.5 deg",
    nbin=11,
)

Again we first illustrate the regions:

plt.figure()
geom = RegionGeom.create(region=regions)
gc_image = counts_image.cutout(
    position=SkyCoord("0d", "0d", frame="galactic"), width=3 * u.deg
)
ax = gc_image.smooth("0.1 deg").plot(stretch="sqrt")
geom.plot_region(ax=ax, color="w")
flux profiles
/home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/regions/core/compound.py:160: UserWarning: Setting the 'color' property will override the edgecolor or facecolor properties.
  patch = mpatches.PathPatch(path, **mpl_kwargs)

<WCSAxes: >

This time we define two energy bins and include the fit statistic profile in the computation:

flux_profile_estimator = FluxProfileEstimator(
    regions=regions,
    spectrum=PowerLawSpectralModel(index=2.3),
    energy_edges=[10, 100, 2000] * u.GeV,
    selection_optional=["ul", "scan"],
    norm_values=np.linspace(-1, 5, 11),
)

profile = flux_profile_estimator.run(datasets=dataset)

We can directly plot the result:

plt.figure()
profile.plot(axis_name="projected-distance", sed_type="flux")
flux profiles
<Axes: xlabel='Projected-distance [deg]', ylabel='flux [1 / (cm2 s)]'>

However because of the powerlaw spectrum the flux at high energies is much lower. To extract the profile at high energies only we can use:

profile_high = profile.slice_by_idx({"energy": slice(1, 2)})

And now plot the points together with the likelihood profiles:

fig, ax = plt.subplots()
profile_high.plot(ax=ax, sed_type="eflux", color="tab:orange")
profile_high.plot_ts_profiles(ax=ax, sed_type="eflux")
ax.set_yscale("linear")


plt.show()

# sphinx_gallery_thumbnail_number = 2
flux profiles

Total running time of the script: ( 0 minutes 25.724 seconds)

Gallery generated by Sphinx-Gallery