.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "tutorials/analysis-2d/detect.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_tutorials_analysis-2d_detect.py: Source detection and significance maps ====================================== Build a list of significant excesses in a Fermi-LAT map. Context ------- The first task in a source catalog production is to identify significant excesses in the data that can be associated to unknown sources and provide a preliminary parametrization in terms of position, extent, and flux. In this notebook we will use Fermi-LAT data to illustrate how to detect candidate sources in counts images with known background. **Objective: build a list of significant excesses in a Fermi-LAT map** Proposed approach ----------------- This notebook show how to do source detection with Gammapy using the methods available in `~gammapy.estimators`. We will use images from a Fermi-LAT 3FHL high-energy Galactic center dataset to do this: - perform adaptive smoothing on counts image - produce 2-dimensional test-statistics (TS) - run a peak finder to detect point-source candidates - compute Li & Ma significance images - estimate source candidates radius and excess counts 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: - `~gammapy.maps.WcsNDMap` - `~gammapy.estimators.ASmoothMapEstimator` - `~gammapy.estimators.TSMapEstimator` - `~gammapy.estimators.utils.find_peaks` .. GENERATED FROM PYTHON SOURCE LINES 45-50 Setup ----- As always, let’s get started with some setup … .. GENERATED FROM PYTHON SOURCE LINES 50-64 .. code-block:: Python import numpy as np import astropy.units as u # %matplotlib inline import matplotlib.pyplot as plt from IPython.display import display from gammapy.datasets import MapDataset from gammapy.estimators import ASmoothMapEstimator, TSMapEstimator from gammapy.estimators.utils import find_peaks, find_peaks_in_flux_map from gammapy.irf import EDispKernelMap, PSFMap from gammapy.maps import Map from gammapy.modeling.models import PointSpatialModel, PowerLawSpectralModel, SkyModel .. GENERATED FROM PYTHON SOURCE LINES 65-67 Check setup ----------- .. GENERATED FROM PYTHON SOURCE LINES 67-72 .. code-block:: Python from gammapy.utils.check import check_tutorials_setup check_tutorials_setup() .. rst-class:: sphx-glr-script-out .. code-block:: none System: python_executable : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/bin/python python_version : 3.9.20 machine : x86_64 system : Linux Gammapy package: version : 1.3 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy Other packages: numpy : 1.26.4 scipy : 1.13.1 astropy : 5.2.2 regions : 0.8 click : 8.1.7 yaml : 6.0.2 IPython : 8.18.1 jupyterlab : not installed matplotlib : 3.9.2 pandas : not installed healpy : 1.17.3 iminuit : 2.30.1 sherpa : 4.16.1 naima : 0.10.0 emcee : 3.1.6 corner : 2.2.3 ray : 2.39.0 Gammapy environment variables: GAMMAPY_DATA : /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/1.3 .. GENERATED FROM PYTHON SOURCE LINES 73-78 Read in input images -------------------- We first read the relevant maps: .. GENERATED FROM PYTHON SOURCE LINES 78-106 .. code-block:: Python counts = Map.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts-cube.fits.gz") background = Map.read( "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-background-cube.fits.gz" ) exposure = Map.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-exposure-cube.fits.gz") psfmap = PSFMap.read( "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-psf-cube.fits.gz", format="gtpsf", ) edisp = EDispKernelMap.from_diagonal_response( energy_axis=counts.geom.axes["energy"], energy_axis_true=exposure.geom.axes["energy_true"], ) dataset = MapDataset( counts=counts, background=background, exposure=exposure, psf=psfmap, name="fermi-3fhl-gc", edisp=edisp, ) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/astropy/wcs/wcs.py:803: FITSFixedWarning: 'datfix' made the change 'Set DATEREF to '2001-01-01T00:01:04.184' from MJDREF. Set MJD-OBS to 54682.655283 from DATE-OBS. Set MJD-END to 57236.967546 from DATE-END'. warnings.warn( .. GENERATED FROM PYTHON SOURCE LINES 107-118 Adaptive smoothing ------------------ For visualisation purpose it can be nice to look at a smoothed counts image. This can be performed using the adaptive smoothing algorithm from `Ebeling et al. (2006) `__. In the following example the `ASmoothMapEstimator.threshold` argument gives the minimum significance expected, values below are clipped. .. GENERATED FROM PYTHON SOURCE LINES 120-129 .. code-block:: Python scales = u.Quantity(np.arange(0.05, 1, 0.05), unit="deg") smooth = ASmoothMapEstimator(threshold=3, scales=scales, energy_edges=[10, 500] * u.GeV) images = smooth.run(dataset) plt.figure(figsize=(9, 5)) images["flux"].plot(add_cbar=True, stretch="asinh") plt.show() .. image-sg:: /tutorials/analysis-2d/images/sphx_glr_detect_001.png :alt: detect :srcset: /tutorials/analysis-2d/images/sphx_glr_detect_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 130-146 TS map estimation ----------------- The Test Statistic, :math:`TS = 2 \Delta log L` (`Mattox et al. 1996 `__), compares the likelihood function L optimized with and without a given source. The TS map is computed by fitting by a single amplitude parameter on each pixel as described in Appendix A of `Stewart (2009) `__. The fit is simplified by finding roots of the derivative of the fit statistics (default settings use `Brent’s method `__). We first need to define the model that will be used to test for the existence of a source. Here, we use a point source. .. GENERATED FROM PYTHON SOURCE LINES 146-154 .. code-block:: Python spatial_model = PointSpatialModel() # We choose units consistent with the map units here... spectral_model = PowerLawSpectralModel(amplitude="1e-22 cm-2 s-1 keV-1", index=2) model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model) .. GENERATED FROM PYTHON SOURCE LINES 155-165 Here we show a full configuration of the estimator. We remind the user of the meaning of the various quantities: - ``model``: a `~gammapy.modeling.models.SkyModel` which is converted to a source model kernel - ``kernel_width``: the width for the above kernel - ``n_sigma``: number of sigma for the flux error - ``n_sigma_ul``: the number of sigma for the flux upper limits - ``selection_optional``: what optional maps to compute - ``n_jobs``: for running in parallel, the number of processes used for the computation - ``sum_over_energy_groups``: to sum over the energy groups or fit the `norm` on the full energy cube .. GENERATED FROM PYTHON SOURCE LINES 168-182 .. code-block:: Python estimator = TSMapEstimator( model=model, kernel_width="1 deg", energy_edges=[10, 500] * u.GeV, n_sigma=1, n_sigma_ul=2, selection_optional=None, n_jobs=1, sum_over_energy_groups=True, ) maps = estimator.run(dataset) .. GENERATED FROM PYTHON SOURCE LINES 183-190 Accessing and visualising results ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Below we print the result of the `~gammapy.estimators.TSMapEstimator`. We have access to a number of different quantities, as shown below. We can also access the quantities names through ``map_result.available_quantities``. .. GENERATED FROM PYTHON SOURCE LINES 190-193 .. code-block:: Python print(maps) .. rst-class:: sphx-glr-script-out .. code-block:: none FluxMaps -------- geom : WcsGeom axes : ['lon', 'lat', 'energy'] shape : (400, 200, 1) quantities : ['ts', 'norm', 'niter', 'norm_err', 'npred', 'npred_excess', 'stat', 'stat_null', 'success'] ref. model : pl n_sigma : 1 n_sigma_ul : 2 sqrt_ts_threshold_ul : 2 sed type init : likelihood .. GENERATED FROM PYTHON SOURCE LINES 195-212 .. code-block:: Python fig, (ax1, ax2, ax3) = plt.subplots( ncols=3, figsize=(20, 3), subplot_kw={"projection": counts.geom.wcs}, gridspec_kw={"left": 0.1, "right": 0.98}, ) maps["sqrt_ts"].plot(ax=ax1, add_cbar=True) ax1.set_title("Significance map") maps["flux"].plot(ax=ax2, add_cbar=True, stretch="sqrt", vmin=0) ax2.set_title("Flux map") maps["niter"].plot(ax=ax3, add_cbar=True) ax3.set_title("Iteration map") plt.show() .. image-sg:: /tutorials/analysis-2d/images/sphx_glr_detect_002.png :alt: Significance map, Flux map, Iteration map :srcset: /tutorials/analysis-2d/images/sphx_glr_detect_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 213-215 The flux in each pixel is obtained by multiplying a reference model with a normalisation factor: .. GENERATED FROM PYTHON SOURCE LINES 215-218 .. code-block:: Python print(maps.reference_model) .. rst-class:: sphx-glr-script-out .. code-block:: none SkyModel Name : VbQh6XbW Datasets names : None Spectral model type : PowerLawSpectralModel Spatial model type : PointSpatialModel Temporal model type : Parameters: index : 2.000 +/- 0.00 amplitude : 1.00e-22 +/- 0.0e+00 1 / (cm2 keV s) reference (frozen): 1.000 TeV lon_0 : 0.000 +/- 0.00 deg lat_0 : 0.000 +/- 0.00 deg .. GENERATED FROM PYTHON SOURCE LINES 220-224 .. code-block:: Python maps.norm.plot(add_cbar=True, stretch="sqrt") plt.show() .. image-sg:: /tutorials/analysis-2d/images/sphx_glr_detect_003.png :alt: detect :srcset: /tutorials/analysis-2d/images/sphx_glr_detect_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 225-234 Source candidates ----------------- Let’s run a peak finder on the `sqrt_ts` image to get a list of point-sources candidates (positions and peak `sqrt_ts` values). The `~gammapy.estimators.utils.find_peaks` function performs a local maximum search in a sliding window, the argument `min_distance` is the minimum pixel distance between peaks (smallest possible value and default is 1 pixel). .. GENERATED FROM PYTHON SOURCE LINES 234-258 .. code-block:: Python sources = find_peaks(maps["sqrt_ts"], threshold=5, min_distance="0.25 deg") nsou = len(sources) display(sources) # Plot sources on top of significance sky image plt.figure(figsize=(9, 5)) ax = maps["sqrt_ts"].plot(add_cbar=True) ax.scatter( sources["ra"], sources["dec"], transform=ax.get_transform("icrs"), color="none", edgecolor="w", marker="o", s=600, lw=1.5, ) plt.show() # sphinx_gallery_thumbnail_number = 3 .. image-sg:: /tutorials/analysis-2d/images/sphx_glr_detect_004.png :alt: detect :srcset: /tutorials/analysis-2d/images/sphx_glr_detect_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none value x y ra dec deg deg ------ --- --- --------- --------- 32.194 200 99 266.41449 -28.97054 27.833 52 60 272.43197 -23.54282 15.16 32 98 271.16056 -21.74479 14.134 69 93 270.40919 -23.47797 13.872 80 92 270.15899 -23.98049 9.7638 273 119 263.18257 -31.52587 8.793 124 102 268.46711 -25.63326 7.3491 123 134 266.97596 -24.77174 6.8071 193 19 270.59696 -30.69138 6.2432 152 148 265.48068 -25.64323 5.8704 230 86 266.15140 -30.58926 5.6678 127 12 272.77351 -27.97934 5.6557 251 139 262.90685 -30.05853 5.4712 181 95 267.17020 -28.26173 5.4209 214 83 266.78188 -29.98429 5.1736 57 49 272.82739 -24.02653 5.067 156 132 266.12148 -26.23306 5.0414 93 80 270.37773 -24.84233 .. GENERATED FROM PYTHON SOURCE LINES 259-261 We can also utilise `~gammapy.estimators.utils.find_peaks_in_flux_map` to display various parameters from the FluxMaps .. GENERATED FROM PYTHON SOURCE LINES 261-266 .. code-block:: Python sources_flux_map = find_peaks_in_flux_map(maps, threshold=5, min_distance="0.25 deg") display(sources_flux_map) .. rst-class:: sphx-glr-script-out .. code-block:: none x y ra dec ... stat_null success flux flux_err deg deg ... 1 / (cm2 s) 1 / (cm2 s) --- --- --------- --------- ... ---------- ------- ----------- ----------- 93 80 270.37773 -24.84233 ... 840.62111 True 8.105e-11 2.332e-11 156 132 266.12148 -26.23306 ... 692.59451 True 5.880e-11 1.859e-11 57 49 272.82739 -24.02653 ... 723.70546 True 5.572e-11 1.729e-11 214 83 266.78188 -29.98429 ... 838.88781 True 9.695e-11 2.583e-11 181 95 267.17020 -28.26173 ... 659.39224 True 1.286e-10 3.165e-11 251 139 262.90685 -30.05853 ... 766.22149 True 6.664e-11 1.913e-11 127 12 272.77351 -27.97934 ... 433.37517 True 4.107e-11 1.349e-11 230 86 266.15140 -30.58926 ... 865.54892 True 1.278e-10 3.040e-11 152 148 265.48068 -25.64323 ... 611.76473 True 7.080e-11 1.876e-11 193 19 270.59696 -30.69138 ... 445.28958 True 6.612e-11 1.711e-11 123 134 266.97596 -24.77174 ... 655.77218 True 9.207e-11 2.123e-11 124 102 268.46711 -25.63326 ... 881.98258 True 1.702e-10 3.050e-11 273 119 263.18257 -31.52587 ... 846.92490 True 1.763e-10 2.947e-11 80 92 270.15899 -23.98049 ... 1093.46225 True 4.576e-10 5.278e-11 69 93 270.40919 -23.47797 ... 1044.25763 True 4.553e-10 5.259e-11 32 98 271.16056 -21.74479 ... 1036.42361 True 5.401e-10 5.794e-11 52 60 272.43197 -23.54282 ... 1092.41995 True 5.984e-10 4.669e-11 200 99 266.41449 -28.97054 ... 137.30287 True 1.414e-09 7.898e-11 .. GENERATED FROM PYTHON SOURCE LINES 267-274 Note that we used the instrument point-spread-function (PSF) as kernel, so the hypothesis we test is the presence of a point source. In order to test for extended sources we would have to use as kernel an extended template convolved by the PSF. Alternatively, we can compute the significance of an extended excess using the Li & Ma formalism, which is faster as no fitting is involve. .. GENERATED FROM PYTHON SOURCE LINES 277-298 What next? ---------- In this notebook, we have seen how to work with images and compute TS and significance images from counts data, if a background estimate is already available. Here’s some suggestions what to do next: - Look how background estimation is performed for IACTs with and without the high level interface in :doc:`/tutorials/starting/analysis_1` and :doc:`/tutorials/starting/analysis_2` notebooks, respectively - Learn about 2D model fitting in the :doc:`/tutorials/analysis-2d/modeling_2D` notebook - Find more about Fermi-LAT data analysis in the :doc:`/tutorials/data/fermi_lat` notebook - Use source candidates to build a model and perform a 3D fitting (see :doc:`/tutorials/analysis-3d/analysis_3d`, :doc:`/tutorials/analysis-3d/analysis_mwl` notebooks for some hints) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 21.188 seconds) .. _sphx_glr_download_tutorials_analysis-2d_detect.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v1.3?urlpath=lab/tree/notebooks/1.3/tutorials/analysis-2d/detect.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: detect.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: detect.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: detect.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_