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Spectral analysis of extended sources

Prerequisites:

• Understanding of spectral analysis techniques in classical Cherenkov astronomy.

• Understanding the basic data reduction and modeling/fitting processes with the gammapy library API as shown in the first gammapy analysis with the gammapy library API tutorial

Context

Many VHE sources in the Galaxy are extended. Studying them with a 1D spectral analysis is more complex than studying point sources. One often has to use complex (i.e. non circular) regions and more importantly, one has to take into account the fact that the instrument response is non uniform over the selectred region. A typical example is given by the supernova remnant RX J1713-3935 which is nearly 1 degree in diameter. See the following article.

Objective: Measure the spectrum of RX J1713-3945 in a 1 degree region fully enclosing it.

Proposed approach:

We have seen in the general presentation of the spectrum extraction for point sources, see the corresponding notebook, that Gammapy uses specific datasets makers to first produce reduced spectral data and then to extract OFF measurements with reflected background techniques: the gammapy.makers.SpectrumDatasetMaker and the gammapy.makers.ReflectedRegionsBackgroundMaker. However if the flag use_region_center is not set to False, the former simply computes the reduced IRFs at the center of the ON region (assumed to be circular).

This is no longer valid for extended sources. To be able to compute average responses in the ON region, we can set use_region_center=False with the gammapy.makers.SpectrumDatasetMaker, in which case the values of the IRFs are averaged over the entire region.

In summary we have to:

• Define an ON region (a ~regions.SkyRegion) fully enclosing the source we want to study.

• Define a gammapy.maps.RegionGeom with the ON region and the required energy range (beware in particular, the true energy range).

• Create the necessary makers :

  • the spectrum dataset maker : gammapy.makers.SpectrumDatasetMaker with use_region_center=False

  • the OFF background maker, here a gammapy.makers.ReflectedRegionsBackgroundMaker

  • and usually the safe range maker : gammapy.makers.SafeRangeMaker

• Perform the data reduction loop. And for every observation:

  • Produce a spectrum dataset

  • Extract the OFF data to produce a gammapy.datasets.SpectrumDatasetOnOff and compute a safe range for it.

  • Stack or store the resulting spectrum dataset.

• Finally proceed with model fitting on the dataset as usual.

Here, we will use the RX J1713-3945 observations from the H.E.S.S. first public test data release. The tutorial is implemented with the intermediate level API.

Setup

As usual, we’ll start with some general imports…

%matplotlib inline
import matplotlib.pyplot as plt

import astropy.units as u
from astropy.coordinates import SkyCoord, Angle
from regions import CircleSkyRegion
from gammapy.maps import MapAxis, RegionGeom
from gammapy.modeling import Fit
from gammapy.data import DataStore
from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
from gammapy.datasets import Datasets, SpectrumDataset
from gammapy.makers import (
    SafeMaskMaker,
    SpectrumDatasetMaker,
    ReflectedRegionsBackgroundMaker,
)

Select the data

We first set the datastore and retrieve a few observations from our source.

datastore = DataStore.from_dir("$GAMMAPY_DATA/hess-dl3-dr1/")
obs_ids = [20326, 20327, 20349, 20350, 20396, 20397]
# In case you want to use all RX J1713 data in the HESS DR1
# other_ids=[20421, 20422, 20517, 20518, 20519, 20521, 20898, 20899, 20900]

observations = datastore.get_observations(obs_ids)

No HDU found matching: OBS_ID = 20326, HDU_TYPE = rad_max, HDU_CLASS = None
No HDU found matching: OBS_ID = 20327, HDU_TYPE = rad_max, HDU_CLASS = None
No HDU found matching: OBS_ID = 20349, HDU_TYPE = rad_max, HDU_CLASS = None
No HDU found matching: OBS_ID = 20350, HDU_TYPE = rad_max, HDU_CLASS = None
No HDU found matching: OBS_ID = 20396, HDU_TYPE = rad_max, HDU_CLASS = None
No HDU found matching: OBS_ID = 20397, HDU_TYPE = rad_max, HDU_CLASS = None

Prepare the datasets creation

Select the ON region

Here we take a simple 1 degree circular region because it fits well with the morphology of RX J1713-3945. More complex regions could be used e.g. ~regions.EllipseSkyRegion or ~regions.RectangleSkyRegion.

target_position = SkyCoord(347.3, -0.5, unit="deg", frame="galactic")
radius = Angle("0.5 deg")
on_region = CircleSkyRegion(target_position, radius)

Define the geometries

This part is especially important. - We have to define first energy axes. They define the axes of the resulting gammapy.datasets.SpectrumDatasetOnOff. In particular, we have to be careful to the true energy axis: it has to cover a larger range than the reconstructed energy one. - Then we define the region geometry itself from the on region.

# The binning of the final spectrum is defined here.
energy_axis = MapAxis.from_energy_bounds(0.1, 40.0, 10, unit="TeV")

# Reduced IRFs are defined in true energy (i.e. not measured energy).
energy_axis_true = MapAxis.from_energy_bounds(
    0.05, 100, 30, unit="TeV", name="energy_true"
)

geom = RegionGeom(on_region, axes=[energy_axis])

Create the makers

First we instantiate the target gammapy.datasets.SpectrumDataset.

dataset_empty = SpectrumDataset.create(
    geom=geom,
    energy_axis_true=energy_axis_true,
)

Now we create its associated maker. Here we need to produce, counts, exposure and edisp (energy dispersion) entries. PSF and IRF background are not needed, therefore we don’t compute them.

IMPORTANT: Note that use_region_center is set to False. This is necessary so that the gammapy.makers.SpectrumDatasetMaker considers the whole region in the IRF computation and not only the center.

maker = SpectrumDatasetMaker(
    selection=["counts", "exposure", "edisp"], use_region_center=False
)

Now we create the OFF background maker for the spectra. If we have an exclusion region, we have to pass it here. We also define the safe range maker.

bkg_maker = ReflectedRegionsBackgroundMaker()
safe_mask_maker = SafeMaskMaker(methods=["aeff-max"], aeff_percent=10)

Perform the data reduction loop.

We can now run over selected observations. For each of them, we: - create the gammapy.datasets.SpectrumDataset - Compute the OFF via the reflected background method and create a gammapy.datasets.SpectrumDatasetOnOff object - Run the safe mask maker on it - Add the gammapy.datasets.SpectrumDatasetOnOff to the list.

%%time
datasets = Datasets()

for obs in observations:
    # A SpectrumDataset is filled in this geometry
    dataset = maker.run(dataset_empty.copy(name=f"obs-{obs.obs_id}"), obs)

    # Define safe mask
    dataset = safe_mask_maker.run(dataset, obs)

    # Compute OFF
    dataset = bkg_maker.run(dataset, obs)

    # Append dataset to the list
    datasets.append(dataset)

CPU times: user 1.92 s, sys: 90.6 ms, total: 2.01 s
Wall time: 2.01 s

datasets.meta_table

Table length=6

NAME	TYPE	TELESCOP	OBS_ID	RA_PNT	DEC_PNT
				deg	deg
str9	str20	str4	int64	float64	float64
obs-20326	SpectrumDatasetOnOff	HESS	20326	259.29852294921875	-39.76222229003906
obs-20327	SpectrumDatasetOnOff	HESS	20327	257.4773254394531	-39.76222229003906
obs-20349	SpectrumDatasetOnOff	HESS	20349	259.29852294921875	-39.76222229003906
obs-20350	SpectrumDatasetOnOff	HESS	20350	257.4773254394531	-39.76222229003906
obs-20396	SpectrumDatasetOnOff	HESS	20396	258.3879089355469	-39.06222152709961
obs-20397	SpectrumDatasetOnOff	HESS	20397	258.3879089355469	-40.462223052978516

Explore the results

We can peek at the content of the spectrum datasets

datasets[0].peek();

Cumulative excess and signficance

Finally, we can look at cumulative significance and number of excesses. This is done with the info_table method of gammapy.datasets.Datasets.

info_table = datasets.info_table(cumulative=True)

info_table

Table length=6

name	counts	background	excess	sqrt_ts	npred	npred_background	npred_signal	exposure_min	exposure_max	livetime	ontime	counts_rate	background_rate	excess_rate	n_bins	n_fit_bins	stat_type	stat_sum	counts_off	acceptance	acceptance_off	alpha
								m2 s	m2 s	s	s	1 / s	1 / s	1 / s
str7	int64	float64	float32	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	int64	int64	str5	float64	int64	float64	float64	float64
stacked	1216	1045.5	170.5	4.159464335903991	1102.3333333333333	1102.3333333333333	nan	4305864.0	422089504.0	1500.0090848207474	1683.0	0.8106617568555015	0.6969957786121931	0.11366597824330839	10	9	wstat	43.26800161601427	2091	9.0	18.0	0.5
stacked	2339	2068.5	270.5	4.7224642932828935	2158.666666666667	2158.666666666667	nan	14651096.0	831745088.0	2997.0830642580986	3366.0	0.7804254836623952	0.6901710615458163	0.09025442211657883	10	9	wstat	72.2340282463576	4137	9.0	18.0	0.5
stacked	3521	3040.5	480.5	6.880790051412004	3200.6666666666665	3200.6666666666665	nan	25027040.0	1240954624.0	4491.585438072681	5048.0	0.7839102803554472	0.6769324644762106	0.10697781587923669	10	9	wstat	121.08402714166986	6081	9.0	18.0	0.5
stacked	4684	4031.0	653.0	8.11478193157773	4248.666666666668	4248.666666666668	nan	29493964.0	1661560320.0	5989.239929735661	6730.0	0.782069186566505	0.6730403268679722	0.10902885969853283	10	9	wstat	159.53811351626462	8062	9.0	18.0	0.5
stacked	5895	5020.33349609375	874.6665	9.869911438596262	5293.754465997458	5293.754465997458	nan	39191584.0	2070336896.0	7488.24085265398	8413.0	0.7872342938743344	0.6704289558627705	0.11680533801156395	10	9	wstat	214.86274893608885	11030	9.0	19.773586770130027	0.4523509740829468
stacked	6985	5991.83349609375	993.1665	10.251113623835666	6305.481315567763	6305.481315567763	nan	41748740.0	2499471872.0	8993.412164390087	10095.0	0.7766796264111517	0.6662469579475904	0.11043266846356133	10	9	wstat	238.19703760223325	12973	9.0	19.48602211261666	0.45818132162094116

fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(121)
ax.plot(
    info_table["livetime"].to("h"),
    info_table["excess"],
    marker="o",
    ls="none",
)

plt.xlabel("Livetime [h]")
plt.ylabel("Excess events")

ax = fig.add_subplot(122)
ax.plot(
    info_table["livetime"].to("h"),
    info_table["sqrt_ts"],
    marker="o",
    ls="none",
)

plt.xlabel("Livetime [h]")
plt.ylabel("Sqrt(TS)");

Perform spectral model fitting

Here we perform a joint fit.

We first create the model, here a simple powerlaw, and assign it to every dataset in the gammapy.datasets.Datasets.

spectral_model = PowerLawSpectralModel(
    index=2, amplitude=2e-11 * u.Unit("cm-2 s-1 TeV-1"), reference=1 * u.TeV
)
model = SkyModel(spectral_model=spectral_model, name="RXJ 1713")

datasets.models = [model]

Now we can run the fit

fit_joint = Fit()
result_joint = fit_joint.run(datasets=datasets)
print(result_joint)

OptimizeResult

        backend    : minuit
        method     : migrad
        success    : True
        message    : Optimization terminated successfully.
        nfev       : 38
        total stat : 52.79

OptimizeResult

        backend    : minuit
        method     : migrad
        success    : True
        message    : Optimization terminated successfully.
        nfev       : 38
        total stat : 52.79

Explore the fit results

First the fitted parameters values and their errors.

datasets.models.to_parameters_table()

Table length=3

model	type	name	value	unit	error	min	max	frozen	link
str8	str8	str9	float64	str14	float64	float64	float64	bool	str1
RXJ 1713	spectral	index	2.1102e+00		6.129e-02	nan	nan	False
RXJ 1713	spectral	amplitude	1.3576e-11	cm-2 s-1 TeV-1	9.757e-13	nan	nan	False
RXJ 1713	spectral	reference	1.0000e+00	TeV	0.000e+00	nan	nan	True

Then plot the fit result to compare measured and expected counts. Rather than plotting them for each individual dataset, we stack all datasets and plot the fit result on the result.

# First stack them all
reduced = datasets.stack_reduce()
# Assign the fitted model
reduced.models = model
# Plot the result

ax_spectrum, ax_residuals = reduced.plot_fit()
reduced.plot_masks(ax=ax_spectrum);