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Source files: detect_ts.ipynb | detect_ts.py

Source detection with Gammapy¶

Introduction¶

This notebook show how to do source detection with Gammapy using one of the methods available in gammapy.detect.

We will do this:

produce 2-dimensional test-statistics (TS) images using Fermi-LAT 3FHL high-energy Galactic center dataset
run a peak finder to make a source catalog
do some simple measurements on each source
compare to the 3FHL catalog

Note that what we do here is a quick-look analysis, the production of real source catalogs use more elaborate procedures.

We will work with the following functions and classes:

Setup¶

As always, let’s get started with some setup …

[1]:

%matplotlib inline
import matplotlib.pyplot as plt

[2]:

from gammapy.maps import Map
from gammapy.detect import TSMapEstimator, find_peaks
from gammapy.catalog import source_catalogs
from gammapy.cube import PSFKernel

Read in input images¶

We first read in the counts cube and sum over the energy axis:

[3]:

counts = Map.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts.fits.gz")
background = Map.read(
    "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-background.fits.gz"
)
exposure = Map.read(
    "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-exposure.fits.gz"
)

maps = {"counts": counts, "background": background, "exposure": exposure}

kernel = PSFKernel.read(
    "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-psf.fits.gz"
)

[4]:

%%time
estimator = TSMapEstimator()
images = estimator.run(maps, kernel.data)

CPU times: user 629 ms, sys: 76 ms, total: 705 ms
Wall time: 9.2 s

Plot images¶

[5]:

plt.figure(figsize=(15, 5))
images["sqrt_ts"].plot(add_cbar=True);

[6]:

plt.figure(figsize=(15, 5))
images["flux"].plot(add_cbar=True, stretch="sqrt", vmin=0);

[7]:

plt.figure(figsize=(15, 5))
images["niter"].plot(add_cbar=True);

Source catalog¶

Let’s run a peak finder on the sqrt_ts image to get a list of sources (positions and peak sqrt_ts values).

[8]:

sources = find_peaks(images["sqrt_ts"], threshold=8)
sources

[8]:

Table length=8

value	x	y	ra	dec
			deg	deg
float64	int64	int64	float64	float64
32.972	200	99	266.41449	-28.97054
27.995	52	60	272.43197	-23.54282
16.18	32	98	271.16056	-21.74479
14.939	69	93	270.40919	-23.47797
14.842	80	92	270.15899	-23.98049
13.951	36	102	270.86716	-21.82076
9.7245	273	119	263.18257	-31.52587
8.8532	124	102	268.46711	-25.63326

[9]:

# Plot sources on top of significance sky image
plt.figure(figsize=(15, 5))

_, ax, _ = images["sqrt_ts"].plot(add_cbar=True)

ax.scatter(
    sources["ra"],
    sources["dec"],
    transform=plt.gca().get_transform("icrs"),
    color="none",
    edgecolor="w",
    marker="o",
    s=600,
    lw=1.5,
);

Measurements¶

TODO: show cutout for a few sources and some aperture photometry measurements (e.g. energy distribution, significance, flux)

[10]:

# TODO

Compare to 3FHL¶

TODO

[11]:

fermi_3fhl = source_catalogs["3fhl"]

[12]:

selection = counts.geom.contains(fermi_3fhl.positions)
fermi_3fhl.table = fermi_3fhl.table[selection]

[13]:

fermi_3fhl.table[["Source_Name", "GLON", "GLAT"]]

[13]:

Table masked=True length=22

Source_Name	GLON	GLAT
	deg	deg
bytes18	float32	float32
3FHL J1731.7-3003	357.4511	1.9489
3FHL J1732.6-3131	356.3192	0.9981
3FHL J1741.8-2536	2.3923	2.4610
3FHL J1744.5-2609	2.2387	1.6537
3FHL J1745.6-2900	359.9423	-0.0497
3FHL J1745.8-3028e	358.7080	-0.8370
3FHL J1746.2-2852	0.1225	-0.0882
3FHL J1747.2-2959	359.2745	-0.8456
3FHL J1747.2-2822	0.6775	-0.0182
...	...	...
3FHL J1753.8-2537	3.7706	0.1398
3FHL J1800.5-2343e	6.1952	-0.2333
3FHL J1800.7-2357	6.0099	-0.3812
3FHL J1801.5-2450	5.3311	-0.9750
3FHL J1801.6-2327	6.5336	-0.3132
3FHL J1802.3-3043	0.2988	-4.0414
3FHL J1803.1-2148	8.1470	0.1906
3FHL J1804.7-2144e	8.3974	-0.0948
3FHL J1809.8-2332	7.3904	-1.9952
3FHL J1811.2-2800	3.6168	-4.4098

Exercises¶

TODO: put one or more exercises

[14]:

# Start exercises here!

What next?¶

In this notebook, we have seen how to work with images and compute TS images from counts data, if a background estimate is already available.

Here’s some suggestions what to do next:

TODO: point to background estimation examples
TODO: point to other docs …