.. 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 : 3.9.19 machine : x86_64 system : Linux Gammapy package: version : 1.3.dev241+g0271bebfc 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 : 1.13.0 astropy : 5.2.2 regions : 0.8 click : 8.1.7 yaml : 6.0.1 IPython : 8.18.1 jupyterlab : not installed matplotlib : 3.8.4 pandas : not installed healpy : 1.16.6 iminuit : 2.25.2 sherpa : 4.16.0 naima : 0.10.0 emcee : 3.1.6 corner : 2.2.2 ray : 2.20.0 Gammapy environment variables: GAMMAPY_DATA : /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/dev .. 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, TS = 2 ∆ 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 146-153 .. 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 154-162 .. code-block:: Python estimator = TSMapEstimator( model, kernel_width="1 deg", energy_edges=[10, 500] * u.GeV, ) maps = estimator.run(dataset) .. GENERATED FROM PYTHON SOURCE LINES 163-166 Plot resulting images ~~~~~~~~~~~~~~~~~~~~~ .. GENERATED FROM PYTHON SOURCE LINES 166-182 .. 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 183-192 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 192-216 .. 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_003.png :alt: detect :srcset: /tutorials/analysis-2d/images/sphx_glr_detect_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none value x y ra dec deg deg ------ --- --- --------- --------- 32.206 200 99 266.41449 -28.97054 27.836 52 60 272.43197 -23.54282 15.171 32 98 271.16056 -21.74479 14.143 69 93 270.40919 -23.47797 13.882 80 92 270.15899 -23.98049 9.7642 273 119 263.18257 -31.52587 8.7947 124 102 268.46711 -25.63326 7.3501 123 134 266.97596 -24.77174 6.8086 193 19 270.59696 -30.69138 6.2444 152 148 265.48068 -25.64323 5.8767 230 86 266.15140 -30.58926 5.6659 127 12 272.77351 -27.97934 5.6556 251 139 262.90685 -30.05853 5.4732 181 95 267.17020 -28.26173 5.4236 214 83 266.78188 -29.98429 5.1755 57 49 272.82739 -24.02653 5.0674 156 132 266.12148 -26.23306 5.0447 93 80 270.37773 -24.84233 .. GENERATED FROM PYTHON SOURCE LINES 217-219 We can also utilise `~gammapy.estimators.utils.find_peaks_in_flux_map` to display various parameters from the FluxMaps .. GENERATED FROM PYTHON SOURCE LINES 219-224 .. 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 8.167e-11 2.347e-11 156 132 266.12148 -26.23306 ... 692.59451 True 5.923e-11 1.872e-11 57 49 272.82739 -24.02653 ... 723.70546 True 5.614e-11 1.740e-11 214 83 266.78188 -29.98429 ... 838.88781 True 9.765e-11 2.599e-11 181 95 267.17020 -28.26173 ... 659.39224 True 1.295e-10 3.186e-11 251 139 262.90685 -30.05853 ... 766.22149 True 6.708e-11 1.925e-11 127 12 272.77351 -27.97934 ... 433.37517 True 4.133e-11 1.357e-11 230 86 266.15140 -30.58926 ... 865.54892 True 1.287e-10 3.058e-11 152 148 265.48068 -25.64323 ... 611.76473 True 7.129e-11 1.889e-11 193 19 270.59696 -30.69138 ... 445.28958 True 6.653e-11 1.721e-11 123 134 266.97596 -24.77174 ... 655.77218 True 9.271e-11 2.137e-11 124 102 268.46711 -25.63326 ... 881.98258 True 1.714e-10 3.070e-11 273 119 263.18257 -31.52587 ... 846.92490 True 1.774e-10 2.965e-11 80 92 270.15899 -23.98049 ... 1093.46225 True 4.609e-10 5.312e-11 69 93 270.40919 -23.47797 ... 1044.25763 True 4.586e-10 5.293e-11 32 98 271.16056 -21.74479 ... 1036.42361 True 5.442e-10 5.833e-11 52 60 272.43197 -23.54282 ... 1092.41995 True 6.023e-10 4.699e-11 200 99 266.41449 -28.97054 ... 137.30287 True 1.424e-09 7.945e-11 .. GENERATED FROM PYTHON SOURCE LINES 225-232 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 235-256 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 15.007 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/main?urlpath=lab/tree/notebooks/dev/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 ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_