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Source files: analysis_mwl.ipynb | analysis_mwl.py
Joint modeling, fitting, and serialization¶
Prerequisites¶
Handling of Fermi-LAT data with gammapy see the corresponding tutorial
Knowledge of spectral analysis to produce 1D On-Off datasets, see the following tutorial
Using flux points to directly fit a model (without forward-folding) see the SED fitting tutorial
Context¶
Some science studies require to combine heterogeneous data from various instruments to extract physical informations. In particular, it is often useful to add flux measurements of a source at different energies to an analysis to better constrain the wide-band spectral parameters. This can be done using a joint fit of heterogeneous datasets.
Objectives: Constrain the spectral parameters of the gamma-ray emission from the Crab nebula between 10 GeV and 100 TeV, using a 3D Fermi dataset, a H.E.S.S. reduced spectrum and HAWC flux points.
Proposed approach¶
This tutorial illustrates how to perfom a joint modeling and fitting of the Crab Nebula spectrum using different datasets. The spectral parameters are optimized by combining a 3D analysis of Fermi-LAT data, a ON/OFF spectral analysis of HESS data, and flux points from HAWC.
In this tutorial we are going to use pre-made datasets. We prepared maps of the Crab region as seen by Fermi-LAT using the same event selection than the 3FHL catalog (7 years of data with energy from 10 GeV to 2 TeV). For the HESS ON/OFF analysis we used two observations from the first public data release with a significant signal from energy of about 600 GeV to 10 TeV. These observations have an offset of 0.5° and a zenith angle of 45-48°. The HAWC flux points data are taken from a recent analysis based on 2.5 years of data with energy between 300 Gev and 300 TeV.
The setup¶
[1]:
from astropy import units as u
import matplotlib.pyplot as plt
from gammapy.modeling import Fit
from gammapy.datasets import Datasets, FluxPointsDataset, SpectrumDatasetOnOff
from gammapy.estimators import (
FluxPoints,
FluxPointsEstimator,
)
from gammapy.maps import MapAxis
from pathlib import Path
Data and models files¶
The datasets serialization produce YAML files listing the datasets and models. In the following cells we show an example containning only the Fermi-LAT dataset and the Crab model.
Fermi-LAT-3FHL_datasets.yaml:
datasets:
- name: Fermi-LAT
type: MapDataset
likelihood: cash
models:
- Crab Nebula
background: background
filename: $GAMMAPY_DATA/fermi-3fhl-crab/Fermi-LAT-3FHL_data_Fermi-LAT.fits
We used as model a point source with a log-parabola spectrum. The initial parameters were taken from the latest Fermi-LAT catalog 4FGL, then we have re-optimized the spectral parameters for our dataset in the 10 GeV - 2 TeV energy range (fixing the source position).
Fermi-LAT-3FHL_models.yaml:
components:
- name: Crab Nebula
type: SkyModel
spatial:
type: PointSpatialModel
frame: icrs
parameters:
- name: lon_0
value: 83.63310241699219
unit: deg
min: .nan
max: .nan
frozen: true
- name: lat_0
value: 22.019899368286133
unit: deg
min: -90.0
max: 90.0
frozen: true
spectral:
type: LogParabolaSpectralModel
parameters:
- name: amplitude
value: 0.3415498620816483
unit: cm-2 s-1 TeV-1
min: .nan
max: .nan
frozen: false
- name: reference
value: 5.054833602905273e-05
unit: TeV
min: .nan
max: .nan
frozen: true
- name: alpha
value: 2.510798031388936
unit: ''
min: .nan
max: .nan
frozen: false
- name: beta
value: -0.022476498188855533
unit: ''
min: .nan
max: .nan
frozen: false
- name: background
type: BackgroundModel
parameters:
- name: norm
value: 0.9544383244743555
unit: ''
min: 0.0
max: .nan
frozen: false
- name: tilt
value: 0.0
unit: ''
min: .nan
max: .nan
frozen: true
- name: reference
value: 1.0
unit: TeV
min: .nan
max: .nan
frozen: true
Reading different datasets¶
Fermi-LAT 3FHL: map dataset for 3D analysis¶
For now we let’s use the datasets serialization only to read the 3D MapDataset associated to Fermi-LAT 3FHL data and models.
[2]:
path = "$GAMMAPY_DATA/fermi-3fhl-crab/Fermi-LAT-3FHL"
filedata = Path(path + "_datasets.yaml")
filemodel = Path(path + "_models.yaml")
datasets = Datasets.read(filedata=filedata, filemodel=filemodel)
dataset_fermi = datasets[0]
[3]:
print(datasets[0].models)
Models
Component 0: SkyModel
Name : Crab Nebula
Datasets names : None
Spectral model type : LogParabolaSpectralModel
Spatial model type : PointSpatialModel
Temporal model type : None
Parameters:
amplitude : 3.42e-01 1 / (cm2 s TeV)
reference (frozen) : 0.000 TeV
alpha : 2.511
beta : -0.022
lon_0 (frozen) : 83.633 deg
lat_0 (frozen) : 22.020 deg
Component 1: BackgroundModel
Name : background
Datasets names : ['Fermi-LAT']
Parameters:
norm : 0.954
tilt (frozen) : 0.000
reference (frozen) : 1.000 TeV
We get the Crab model in order to share it with the other datasets
[4]:
crab_model = dataset_fermi.models["Crab Nebula"]
crab_spec = crab_model.spectral_model
print(crab_spec)
LogParabolaSpectralModel
name value unit min max frozen error
--------- ---------- -------------- --- --- ------ ---------
amplitude 3.415e-01 cm-2 s-1 TeV-1 nan nan False 0.000e+00
reference 5.055e-05 TeV nan nan True 0.000e+00
alpha 2.511e+00 nan nan False 0.000e+00
beta -2.248e-02 nan nan False 0.000e+00
HESS-DL3: 1D ON/OFF dataset for spectral fitting¶
The ON/OFF datasets can be read from PHA files following the OGIP standards. We read the PHA files from each observation, and compute a stacked dataset for simplicity. Then the Crab spectral model previously defined is added to the dataset.
[5]:
datasets = []
for obs_id in [23523, 23526]:
dataset = SpectrumDatasetOnOff.from_ogip_files(
f"$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs{obs_id}.fits"
)
datasets.append(dataset)
dataset_hess = Datasets(datasets).stack_reduce(name="HESS")
dataset_hess.models = crab_model
/Users/terrier/Code/gammapy-dev/gammapy-docs/build/v0.17/gammapy/gammapy/utils/interpolation.py:163: RuntimeWarning: overflow encountered in log
return np.log(values)
HAWC: 1D dataset for flux point fitting¶
The HAWC flux point are taken from https://arxiv.org/pdf/1905.12518.pdf. Then these flux points are read from a pre-made FITS file and passed to a FluxPointsDataset together with the source spectral model.
[6]:
# read flux points from https://arxiv.org/pdf/1905.12518.pdf
filename = "$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits"
flux_points_hawc = FluxPoints.read(filename)
dataset_hawc = FluxPointsDataset(crab_model, flux_points_hawc, name="HAWC")
Datasets serialization¶
The datasets object contains each dataset previously defined. It can be saved on disk as datasets.yaml, models.yaml, and several data files specific to each dataset. Then the datasets can be rebuild later from these files.
[7]:
datasets = Datasets([dataset_fermi, dataset_hess, dataset_hawc])
path = Path("crab-3datasets")
path.mkdir(exist_ok=True)
datasets.write(path=path, prefix="crab_10GeV_100TeV", overwrite=True)
filedata = path / "crab_10GeV_100TeV_datasets.yaml"
filemodel = path / "crab_10GeV_100TeV_models.yaml"
datasets = Datasets.read(filedata=filedata, filemodel=filemodel)
[8]:
print(datasets["HESS"].models)
Models
Component 0: SkyModel
Name : Crab Nebula
Datasets names : None
Spectral model type : LogParabolaSpectralModel
Spatial model type : PointSpatialModel
Temporal model type : None
Parameters:
amplitude : 3.42e-01 1 / (cm2 s TeV)
reference (frozen) : 0.000 TeV
alpha : 2.511
beta : -0.022
lon_0 (frozen) : 83.633 deg
lat_0 (frozen) : 22.020 deg
Component 1: BackgroundModel
Name : background
Datasets names : ['Fermi-LAT']
Parameters:
norm : 0.954
tilt (frozen) : 0.000
reference (frozen) : 1.000 TeV
Joint analysis¶
We run the fit on the Datasets object that include a dataset for each instrument
[9]:
%%time
fit_joint = Fit(datasets)
results_joint = fit_joint.run()
print(results_joint)
print(results_joint.parameters.to_table())
OptimizeResult
backend : minuit
method : minuit
success : True
message : Optimization terminated successfully.
nfev : 829
total stat : -14819.11
name value unit min max frozen error
--------- --------- -------------- ---------- --------- ------ ---------
amplitude 4.015e-03 cm-2 s-1 TeV-1 nan nan False 1.072e-04
reference 5.055e-05 TeV nan nan True 0.000e+00
alpha 1.260e+00 nan nan False 7.101e-04
beta 6.185e-02 nan nan False 1.962e-04
lon_0 8.363e+01 deg nan nan True 0.000e+00
lat_0 2.202e+01 deg -9.000e+01 9.000e+01 True 0.000e+00
norm 9.839e-01 0.000e+00 nan False 3.239e-01
tilt 0.000e+00 nan nan True 0.000e+00
reference 1.000e+00 TeV nan nan True 0.000e+00
CPU times: user 12.6 s, sys: 105 ms, total: 12.7 s
Wall time: 12.7 s
Let’s display only the parameters of the Crab spectral model
[10]:
crab_spec = datasets[0].models["Crab Nebula"].spectral_model
print(crab_spec)
LogParabolaSpectralModel
name value unit min max frozen error
--------- --------- -------------- --- --- ------ ---------
amplitude 4.015e-03 cm-2 s-1 TeV-1 nan nan False 1.072e-04
reference 5.055e-05 TeV nan nan True 0.000e+00
alpha 1.260e+00 nan nan False 7.101e-04
beta 6.185e-02 nan nan False 1.962e-04
We can compute flux points for Fermi-LAT and HESS datasets in order plot them together with the HAWC flux point.
[11]:
# compute Fermi-LAT and HESS flux points
e_edges = MapAxis.from_bounds(
0.01, 2.0, nbin=6, interp="log", unit="TeV"
).edges
flux_points_fermi = FluxPointsEstimator(
e_edges=e_edges, source="Crab Nebula"
).run([dataset_fermi])
e_edges = MapAxis.from_bounds(1, 15, nbin=6, interp="log", unit="TeV").edges
flux_points_hess = FluxPointsEstimator(
e_edges=e_edges, source="Crab Nebula"
).run([dataset_hess])
Now, Let’s plot the Crab spectrum fitted and the flux points of each instrument.
[12]:
# display spectrum and flux points
energy_range = [0.01, 120] * u.TeV
plt.figure(figsize=(8, 6))
ax = crab_spec.plot(energy_range=energy_range, energy_power=2, label="Model")
crab_spec.plot_error(ax=ax, energy_range=energy_range, energy_power=2)
flux_points_fermi.plot(ax=ax, energy_power=2, label="Fermi-LAT")
flux_points_hess.plot(ax=ax, energy_power=2, label="HESS")
flux_points_hawc.plot(ax=ax, energy_power=2, label="HAWC")
plt.legend();
[ ]: