{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to perform spectral component separation?" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "
\"Download
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial you will learn how to use spectral component separation to disentangle the morphological properties of overlapping sources.\n", "\n", "As usual we start by importing the gammalib, ctools, and cscripts Python modules." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import gammalib\n", "import ctools\n", "import cscripts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also import the matplotlib package for plotting." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulated dataset\n", "\n", "For the tutorial we will simulate a small CTA dataset. We start by defining the Instrument Response Functions and energy range that will be used all along." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "caldb = 'prod2'\n", "irf = 'South_5h'\n", "emin = 0.1 # TeV\n", "emax = 160.0 # TeV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will simulate an observation of the region around the famous sources HESS J1825-137 and HESS J1826-130 based on a very simple sky model. We will consider two pointings of a few hours wobbling around the sources' position.\n", "\n", "We start by writing the pointing directions and times to an ASCII file." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "pointing_file = 'pointings.txt'\n", "\n", "# open file\n", "f = open(pointing_file, 'w')\n", "# header\n", "f.write('id,ra,dec,tmin,tmax\\n')\n", "# pointings\n", "f.write('0001,275.65,-13.78,0.,10800.\\n')\n", "f.write('0002,277.25,-13.78,11000.,21800.\\n')\n", "\n", "# close file\n", "f.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we use the csobsdef script to convert the list of pointings into an observation definition XML file." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "obsdef = cscripts.csobsdef()\n", "obsdef['inpnt'] = pointing_file\n", "obsdef['caldb'] = caldb\n", "obsdef['irf'] = irf\n", "obsdef['emin'] = emin\n", "obsdef['emax'] = emax\n", "obsdef['rad'] = 5.\n", "obsdef.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we use ctobssim to perform the observation simulation." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "obssim = ctools.ctobssim(obsdef.obs())\n", "obssim['inmodel'] = '$CTOOLS/share/models/hess1825_26.xml'\n", "\n", "obssim.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Skymap inspection and preliminary likelihood fit\n", "\n", "*This section showcases a possible path to get to perform a spectral component separation. If you are here because you have an application in which you know the spectrum of your sources of interest, and you want just to learn how to perform the spectral component separation, please jump to the next section.*\n", "\n", "As usual a great way to inspect the data is making a skymap using ctskymap." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "skymap = ctools.ctskymap(obssim.obs())\n", "skymap['emin'] = emin\n", "skymap['emax'] = emax\n", "skymap['nxpix'] = 200\n", "skymap['nypix'] = 200\n", "skymap['binsz'] = 0.02\n", "skymap['proj'] = 'TAN'\n", "skymap['coordsys'] = 'CEL'\n", "skymap['xref'] = 276.45\n", "skymap['yref'] = -13.78\n", "skymap['bkgsubtract'] = 'NONE'\n", "skymap.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we inspect the skymap by using matpltolib." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Slightly smooth the map for display to suppress statistical fluctuations\n", "skymap.skymap().smooth('GAUSSIAN',0.1)\n", "\n", "fig = plt.figure()\n", "ax = plt.subplot()\n", "plt.imshow(skymap.skymap().array(),origin='lower',\n", " extent=[276.45+0.02*100,276.45-0.02*100,-13.78-0.02*100,-13.78+0.02*100])\n", " # boundaries of the coord grid\n", "ax.set_xlabel('R.A. (deg)')\n", "ax.set_ylabel('Dec (deg)')\n", "cbar = plt.colorbar()\n", "cbar.set_label('Counts')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A large blob of emission appears at the center of the map. It coincides with the source HESS J1825-137. Past observations have indicated that HESS J1826-130 has a harder spectrum than HESS J1825-137. Let's peek at a skymap above 10 TeV." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "skymap = ctools.ctskymap(obssim.obs())\n", "skymap['emin'] = 10. #TeV\n", "skymap['emax'] = emax\n", "skymap['nxpix'] = 200\n", "skymap['nypix'] = 200\n", "skymap['binsz'] = 0.02\n", "skymap['proj'] = 'TAN'\n", "skymap['coordsys'] = 'CEL'\n", "skymap['xref'] = 276.45\n", "skymap['yref'] = -13.78\n", "skymap['bkgsubtract'] = 'NONE'\n", "skymap.run()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Slightly smooth the map for display to suppress statistical fluctuations\n", "skymap.skymap().smooth('GAUSSIAN',0.1)\n", "\n", "fig = plt.figure()\n", "ax = plt.subplot()\n", "plt.imshow(skymap.skymap().array(),origin='lower',\n", " extent=[276.45+0.02*100,276.45-0.02*100,-13.78-0.02*100,-13.78+0.02*100])\n", " # boundaries of the coord grid\n", "ax.set_xlabel('R.A. (deg)')\n", "ax.set_ylabel('Dec (deg)')\n", "cbar = plt.colorbar()\n", "cbar.set_label('Counts')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed, emission above 10 TeV is concentrated on a spot North of the low-energy blob coincident with the position of HESS J1826-130. How can we disentangle the morphology of the two sources based on our observations?\n", "\n", "One of the traditional techniques to disentangle multiple overlapping sources is using a 3D likelihood analysis based on some guess of the sources' morphology and spectra. However, this often implies to choose a simplistic (analytical) model for the sources' morphology. Below we will test another approach, that consists in determining the morphology from the data using some knowledge of their spectra.\n", "\n", "For our exercise we guess the spectra of the two components via a preliminary likelihood analysis. For this we use a tentative sky model inspired by the skymaps we have inspected. We include in the model two disks with centre and extension eye-balled from the low-energy blob and high-energy spot. Their parameters are not fit to the data here for the sake of a shorter execution time. The morphology will be determined from the data later. However, at a cost of a longer computing time you could fit the spatial model parameters in this step.\n", "\n", "For both components we will assume a power-law spectrum, with and index of 2.5 for the soft component and an index of 1.5 for the hard component, and a flux at 1 TeV approximately 10% and 1% of the Crab nebula, respectively. These spectral parameters are going to be fit to the data." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " >" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# model container\n", "models = gammalib.GModels()\n", "\n", "# low-energy blob\n", "centre = gammalib.GSkyDir()\n", "centre.radec_deg(276.5,-13.75)\n", "spatial = gammalib.GModelSpatialRadialDisk(centre,0.5)\n", "# free source centre\n", "spatial['RA'].fix()\n", "spatial['DEC'].fix()\n", "spatial['Radius'].fix()\n", "spectral = gammalib.GModelSpectralPlaw(4.e-18,-2.5,gammalib.GEnergy(1.,'TeV'))\n", "source = gammalib.GModelSky(spatial,spectral)\n", "source.name('HESS J1825-137')\n", "models.append(source)\n", "\n", "# high-energy spot\n", "centre = gammalib.GSkyDir()\n", "centre.radec_deg(276.5,-13)\n", "spatial = gammalib.GModelSpatialRadialDisk(centre,0.1)\n", "# free source centre\n", "spatial['RA'].fix()\n", "spatial['DEC'].fix()\n", "spatial['Radius'].fix()\n", "spectral = gammalib.GModelSpectralPlaw(4.e-19,-1.5,gammalib.GEnergy(1.,'TeV'))\n", "source = gammalib.GModelSky(spatial,spectral)\n", "source.name('HESS J1826-130')\n", "models.append(source)\n", "\n", "# instrumental background\n", "# power law spectral correction with pivot energy at 1 TeV\n", "spectral = gammalib.GModelSpectralPlaw(1, 0, gammalib.GEnergy(1, 'TeV'))\n", "bkgmodel = gammalib.GCTAModelIrfBackground(spectral)\n", "bkgmodel.name('Background')\n", "bkgmodel.instruments('CTA')\n", "# append to models\n", "models.append(bkgmodel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We copy the simulated observations and append to them our initial sky model." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "obs = obssim.obs().copy()\n", "obs.models(models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to start with a stacked analysis. We bin the events, and then attach to the stacked observations the stacked response. Note that if the dataset is small it may be convenient to use an unbinned analysis in lieu of the stacked analysis for this step." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Bin events\n", "cntcube = ctools.ctbin(obs)\n", "cntcube['usepnt'] = False\n", "cntcube['ebinalg'] = 'LOG'\n", "cntcube['xref'] = 276.45\n", "cntcube['yref'] = -13.78\n", "cntcube['binsz'] = 0.02\n", "cntcube['nxpix'] = 200\n", "cntcube['nypix'] = 200\n", "cntcube['enumbins'] = 40\n", "cntcube['emin'] = emin\n", "cntcube['emax'] = emax\n", "cntcube['coordsys'] = 'CEL'\n", "cntcube['proj'] = 'TAN'\n", "cntcube.run()\n", "\n", "# Extract counts cube\n", "cube = cntcube.cube()\n", "\n", "# Compute stacked response\n", "response = cscripts.obsutils.get_stacked_response(obs,cube)\n", "\n", "# Copy stacked observations\n", "stacked_obs = cntcube.obs().copy()\n", "\n", "# Append stacked response\n", "stacked_obs[0].response(response['expcube'], response['psfcube'],response['bkgcube'])\n", "\n", "# Set stacked models\n", "stacked_obs.models(response['models'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can run the preliminary likelihood analysis in which the spectral parameters for the two sources are fit to the data." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "like = ctools.ctlike(stacked_obs)\n", "like.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check that the fit was successful." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== GOptimizerLM ===\n", " Optimized function value ..: 122722.355\n", " Absolute precision ........: 0.005\n", " Acceptable value decrease .: 2\n", " Optimization status .......: converged\n", " Number of parameters ......: 18\n", " Number of free parameters .: 6\n", " Number of iterations ......: 6\n", " Lambda ....................: 1e-09\n" ] } ], "source": [ "print(like.opt())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also check the fitted models." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== GModels ===\n", " Number of models ..........: 3\n", " Number of parameters ......: 18\n", "=== GModelSky ===\n", " Name ......................: HESS J1825-137\n", " Instruments ...............: all\n", " Observation identifiers ...: all\n", " Model type ................: ExtendedSource\n", " Model components ..........: \"RadialDisk\" * \"PowerLaw\" * \"Constant\"\n", " Number of parameters ......: 7\n", " Number of spatial par's ...: 3\n", " RA .......................: 276.5 deg (fixed,scale=1)\n", " DEC ......................: -13.75 deg (fixed,scale=1)\n", " Radius ...................: 0.5 [0.0002778,infty[ deg (fixed,scale=1)\n", " Number of spectral par's ..: 3\n", " Prefactor ................: 6.34330444443657e-18 +/- 3.01336043884925e-19 [0,infty[ ph/cm2/s/MeV (free,scale=4e-18,gradient)\n", " Index ....................: -2.344118164095 +/- 0.0376367951224068 [10,-10] (free,scale=-2.5,gradient)\n", " PivotEnergy ..............: 1000000 MeV (fixed,scale=1000000,gradient)\n", " Number of temporal par's ..: 1\n", " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n", " Number of scale par's .....: 0\n", "=== GModelSky ===\n", " Name ......................: HESS J1826-130\n", " Instruments ...............: all\n", " Observation identifiers ...: all\n", " Model type ................: ExtendedSource\n", " Model components ..........: \"RadialDisk\" * \"PowerLaw\" * \"Constant\"\n", " Number of parameters ......: 7\n", " Number of spatial par's ...: 3\n", " RA .......................: 276.5 deg (fixed,scale=1)\n", " DEC ......................: -13 deg (fixed,scale=1)\n", " Radius ...................: 0.1 [0.0002778,infty[ deg (fixed,scale=1)\n", " Number of spectral par's ..: 3\n", " Prefactor ................: 2.36040613319893e-19 +/- 9.2386146949274e-20 [0,infty[ ph/cm2/s/MeV (free,scale=4e-19,gradient)\n", " Index ....................: -1.6532558684395 +/- 0.144999501997818 [10,-10] (free,scale=-1.5,gradient)\n", " PivotEnergy ..............: 1000000 MeV (fixed,scale=1000000,gradient)\n", " Number of temporal par's ..: 1\n", " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n", " Number of scale par's .....: 0\n", "=== GCTAModelCubeBackground ===\n", " Name ......................: BackgroundModel\n", " Instruments ...............: CTA, HESS, MAGIC, VERITAS\n", " Observation identifiers ...: all\n", " Model type ................: \"PowerLaw\" * \"Constant\"\n", " Number of parameters ......: 4\n", " Number of spectral par's ..: 3\n", " Prefactor ................: 1.24199768427314 +/- 0.0147414039983284 [0.01,100] ph/cm2/s/MeV (free,scale=1,gradient)\n", " Index ....................: 0.103013198377256 +/- 0.00683960305372905 [-5,5] (free,scale=1,gradient)\n", " PivotEnergy ..............: 1000000 MeV (fixed,scale=1000000,gradient)\n", " Number of temporal par's ..: 1\n", " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n" ] } ], "source": [ "print(like.obs().models())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As guessed from the skymaps HESS J1826-130 is fainter than HESS J1825-137, but its spectrum is rather harder. We will use the values of the spectral indices obtained from this likelihood analysis to derive the source morphology below.\n", "\n", "However, we first check the fit residuals. Let's start by inspecting the spectral residuals using csresspec." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "resspec = cscripts.csresspec(like.obs())\n", "resspec['algorithm'] = 'SIGNIFICANCE'\n", "resspec['components'] = True\n", "resspec['outfile'] = 'resspec.fits'\n", "resspec.execute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use an example script to display the residuals." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import sys\n", "import os\n", "sys.path.append(os.environ['CTOOLS']+'/share/examples/python/')\n", "\n", "from show_residuals import plot_residuals\n", "plot_residuals('resspec.fits','',0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model reproduces reasonably well the data spectrally (although not perfectly). We will also check the spatial residuals using csresmap." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "resmap = cscripts.csresmap(like.obs())\n", "resmap['algorithm'] = 'SIGNIFICANCE'\n", "resmap.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We inspect the map to check the spatial residuals." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Slightly smooth the map for display to suppress statistical fluctuations\n", "resid = resmap._resmap.copy()\n", "resid.smooth('GAUSSIAN',0.1)\n", "# Plotting\n", "fig = plt.figure()\n", "ax = plt.subplot()\n", "plt.imshow(resid.array(),origin='lower', cmap='bwr',\n", " extent=[276.45+0.02*100,276.45-0.02*100,-13.78-0.02*100,-13.78+0.02*100])\n", " # Boundaries of the coord grid\n", "ax.set_xlabel('R.A. (deg)')\n", "ax.set_ylabel('Dec (deg)')\n", "cbar = plt.colorbar()\n", "cbar.set_label('Significance ($\\sigma$)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The spatial residuals are small, indicating that the model is close enough to the data. However, the structures in the residuals indicate that the morphological models adopted may not accurately represent the data.\n", "\n", "## Stacked spectral component separation\n", "\n", "If we are confident that the spectral models we have at hand represent the data well, we can use this information to determine the morphology of the emission associated with each spectral component from the data by using csscs. First of all we will perform the spectral component separation using the stacked dataset. **A stacked analysis is usually the fastest option for csscs**. However, an unbinned analysis is also possible. \n", "\n", "As a preliminary step we will change the spatial models for the two components to be separated. The script uses the spatial model provided as prior, so for a disk the flux outside the disk radius would always be null. If you have a reasonable prior you can use it in the component separation, but **it is recommended to use a spatial model without sharp boundaries (e.g., Gaussian)**. Since in this case we do not know what the morphology of the two sources is, we will start with an isotropic distribution. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# copy the fitted models\n", "fit_obs = like.obs().copy()\n", "\n", "# replace disks with isotropic model\n", "for model in fit_obs.models():\n", " if model.name() == 'HESS J1825-137' or model.name() == 'HESS J1826-130': \n", " model.spatial(gammalib.GModelSpatialDiffuseConst())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The essential information to be provided to csscs is:\n", "\n", "- the geometry of the output maps; for every bin in the output map a dedicated likelhood analysis will be used to determine the fluxes of the sources of interest; **make sure to have a region just big enough and a grid step just fine enough for your purposes** since increasing the number of bins will proportionally increase the computation time; \n", "\n", "- the list of the sources of interest;\n", "\n", "- the energy range for the analysis;\n", "\n", "- the radius (``rad``) of the region of interest to be used for the spectral component separation centred on each bin of the output maps; note that the value of this parameter sets the correlation scale between neighbour pixels in the output maps, and it must be at least sqrt(2) times ``binsz`` to fully cover the input dataset; furthemore, **``rad`` must be large enough to ensure that you have enough statististics to perform a likelihood fit**.\n", "\n", "You can decide if you want to leave free in the fit the background model (true by default) and other background gamma-ray sources (false by default, we have none in the exemple we are considering)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "scs1 = cscripts.csscs(fit_obs)\n", "scs1['srcnames'] = 'HESS J1825-137;HESS J1826-130'\n", "scs1['emin'] = emin\n", "scs1['emax'] = emax\n", "scs1['nxpix'] = 20\n", "scs1['nypix'] = 20\n", "scs1['binsz'] = 0.1\n", "scs1['rad'] = 0.2\n", "scs1['proj'] = 'TAN'\n", "scs1['coordsys'] = 'CEL'\n", "scs1['xref'] = 276.45\n", "scs1['yref'] = -13.78\n", "scs1.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can inspect the maps of the fluxes from the two sources. We will set a minimum flux for display to avoid being confused by noisy bins. In fact the script has calculated also the flux uncertainty in each bin (accessible through the ``flux_error`` method) and the detection significance (accessible through the ``ts`` method), that you can use to filter more intelligently the maps. We did not request this, but one could also have flux upper limits computed (``calc_ulimit`` parameter)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flux_1826 = scs1.flux('HESS J1826-130')\n", "\n", "# Plotting\n", "fig = plt.figure()\n", "ax = plt.subplot()\n", "plt.imshow(flux_1826.array(),origin='lower', vmin = 1.e-8,\n", " extent=[276.45+0.1*10,276.45-0.1*10,-13.78-0.1*10,-13.78+0.1*10])\n", " # Boundaries of the coord grid\n", "ax.set_xlabel('R.A. (deg)')\n", "ax.set_ylabel('Dec (deg)')\n", "cbar = plt.colorbar()\n", "cbar.set_label('Flux (photons/cm$^2$/s/sr)')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flux_1825 = scs1.flux('HESS J1825-137')\n", "\n", "# Plotting\n", "fig = plt.figure()\n", "ax = plt.subplot()\n", "plt.imshow(flux_1825.array(),origin='lower', vmin = 1.e-8,\n", " extent=[276.45+0.1*10,276.45-0.1*10,-13.78-0.1*10,-13.78+0.1*10])\n", " # Boundaries of the coord grid\n", "ax.set_xlabel('R.A. (deg)')\n", "ax.set_ylabel('Dec (deg)')\n", "cbar = plt.colorbar()\n", "cbar.set_label('Flux (photons/cm$^2$/s/sr)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the hard emission is confined in the North. The soft emission blob seems to have an elongated shape.\n", "\n", "## On/Off spectral component separation\n", "\n", "What if you don't have a reliable background model to perform a 3D analysis? You can still perform a spectral component separation using the On/Off technique as long as you have a prior on the source's spectra.\n", "\n", "For this **we need first of all to compute an exclusion map**, i.e., a map of the region where there is significant gamma-ray emission, so that we can exclude it from background computation. We can do this by using ctskymap with the ``RING`` background subtraction method. Since we are dealing with a large source we will use rather large ROI and ring regions." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "skymap = ctools.ctskymap(obssim.obs())\n", "skymap['emin'] = emin\n", "skymap['emax'] = emax\n", "skymap['nxpix'] = 200\n", "skymap['nypix'] = 200\n", "skymap['binsz'] = 0.02\n", "skymap['proj'] = 'TAN'\n", "skymap['coordsys'] = 'CEL'\n", "skymap['xref'] = 276.45\n", "skymap['yref'] = -13.78\n", "skymap['bkgsubtract'] = 'RING'\n", "skymap['roiradius'] = 0.5\n", "skymap['inradius'] = 1.0\n", "skymap['outradius'] = 1.5\n", "skymap['iterations'] = 3\n", "skymap['threshold'] = 5 # sigma\n", "skymap.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's inspect the exclusion map." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Dec (deg)')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = plt.subplot()\n", "plt.imshow(skymap.exclusion_map().map().array(), origin='lower', cmap = 'binary',\n", " extent=[276.45+0.02*100,276.45-0.02*100,-13.48-0.02*100,-13.48+0.02*100])\n", " # boundaries of the coord grid\n", "ax.set_xlabel('R.A. (deg)')\n", "ax.set_ylabel('Dec (deg)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use csscs in On/Off mode we need to go back to using the event lists. We modify the associated models to the latest version obtained from the global likelihood fit." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "obs.models(like.obs().models())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that **in On/Off mode if there are multiple sources csscs will use only the spectral models for the sources, and their emission within each ROI for component separation will be assumed by default to be isotropic**. We will not use the background model, we'll just assume that the background rate of the reflected background regions is the same as in the ROI.\n", "\n", "*This step is rather time consuming, and should take at least a couple of hours to complete on a normal laptop. This is a good time for you to grab lunch or to read that review paper you always meant to.*" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "scs2 = cscripts.csscs(obs)\n", "scs2['srcnames'] = 'HESS J1825-137;HESS J1826-130'\n", "scs2['emin'] = emin\n", "scs2['emax'] = emax\n", "scs2['nxpix'] = 20\n", "scs2['nypix'] = 20\n", "scs2['binsz'] = 0.1\n", "scs2['rad'] = 0.2\n", "scs2['proj'] = 'TAN'\n", "scs2['coordsys'] = 'CEL'\n", "scs2['xref'] = 276.45\n", "scs2['yref'] = -13.78\n", "scs2['method'] = 'ONOFF'\n", "scs2['use_model_bkg'] = False\n", "scs2['enumbins'] = 30\n", "scs2.exclusion_map(skymap.exclusion_map())\n", "scs2.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We visualize below the flux maps, which are quite consistent with those obtained using the stacked analysis." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flux_1826 = scs2.flux('HESS J1826-130')\n", "\n", "# Plotting\n", "fig = plt.figure()\n", "ax = plt.subplot()\n", "plt.imshow(flux_1826.array(),origin='lower', vmin = 1.e-8,\n", " extent=[276.45+0.1*10,276.45-0.1*10,-13.78-0.1*10,-13.78+0.1*10])\n", " # Boundaries of the coord grid\n", "ax.set_xlabel('R.A. (deg)')\n", "ax.set_ylabel('Dec (deg)')\n", "cbar = plt.colorbar()\n", "cbar.set_label('Flux (photons/cm$^2$/s/sr)')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flux_1825 = scs1.flux('HESS J1825-137')\n", "\n", "# Plotting\n", "fig = plt.figure()\n", "ax = plt.subplot()\n", "plt.imshow(flux_1825.array(),origin='lower', vmin = 1.e-8,\n", " extent=[276.45+0.1*10,276.45-0.1*10,-13.78-0.1*10,-13.78+0.1*10])\n", " # Boundaries of the coord grid\n", "ax.set_xlabel('R.A. (deg)')\n", "ax.set_ylabel('Dec (deg)')\n", "cbar = plt.colorbar()\n", "cbar.set_label('Flux (photons/cm$^2$/s/sr)')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }