{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "**This is a fixed-text formatted version of a Jupyter notebook**\n", "\n", "- Try online[![Binder](https://static.mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.19?urlpath=lab/tree/tutorials/data/fermi_lat.ipynb)\n", "- You may download all the notebooks in the documentation as a\n", "[tar file](../../_downloads/notebooks-0.19.tar).\n", "- **Source files:**\n", "[fermi_lat.ipynb](../../_static/notebooks/fermi_lat.ipynb) |\n", "[fermi_lat.py](../../_static/notebooks/fermi_lat.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fermi-LAT with Gammapy\n", "\n", "## Introduction\n", "\n", "This tutorial will show you how to work with Fermi-LAT data with Gammapy. As an example, we will look at the Galactic center region using the high-energy dataset that was used for the 3FHL catalog, in the energy range 10 GeV to 2 TeV.\n", "\n", "We note that support for Fermi-LAT data analysis in Gammapy is very limited. For most tasks, we recommend you use \n", "[Fermipy](http://fermipy.readthedocs.io/), which is based on the [Fermi Science Tools](https://fermi.gsfc.nasa.gov/ssc/data/analysis/software/) (Fermi ST).\n", "\n", "Using Gammapy with Fermi-LAT data could be an option for you if you want to do an analysis that is not easily possible with Fermipy and the Fermi Science Tools. For example a joint likelihood fit of Fermi-LAT data with data e.g. from H.E.S.S., MAGIC, VERITAS or some other instrument, or analysis of Fermi-LAT data with a complex spatial or spectral model that is not available in Fermipy or the Fermi ST.\n", "\n", "Besides Gammapy, you might want to look at are [Sherpa](http://cxc.harvard.edu/sherpa/) or [3ML](https://threeml.readthedocs.io/). Or just using Python to roll your own analysis using several existing analysis packages. E.g. it it possible to use Fermipy and the Fermi ST to evaluate the likelihood on Fermi-LAT data, and Gammapy to evaluate it e.g. for IACT data, and to do a joint likelihood fit using e.g. [iminuit](http://iminuit.readthedocs.io/) or [emcee](http://dfm.io/emcee).\n", "\n", "To use Fermi-LAT data with Gammapy, you first have to use the Fermi ST to prepare an event list (using ``gtselect`` and ``gtmktime``, exposure cube (using ``gtexpcube2`` and PSF (using ``gtpsf``). You can then use `~gammapy.data.EventList`, `~gammapy.maps` and the `~gammapy.irf.PSFMap` to read the Fermi-LAT maps and PSF, i.e. support for these high level analysis products from the Fermi ST is built in. To do a 3D map analysis, you can use Fit for Fermi-LAT data in the same way that it's use for IACT data. This is illustrated in this notebook. A 1D region-based spectral analysis is also possible, this will be illustrated in a future tutorial.\n", "\n", "## Setup\n", "\n", "**IMPORTANT**: For this notebook you have to get the prepared ``3fhl`` dataset provided in your $GAMMAPY_DATA.\n", "\n", "Note that the ``3fhl`` dataset is high-energy only, ranging from 10 GeV to 2 TeV." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:14.601985Z", "iopub.status.busy": "2021-11-22T21:10:14.601225Z", "iopub.status.idle": "2021-11-22T21:10:14.726068Z", "shell.execute_reply": "2021-11-22T21:10:14.726475Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fermi_3fhl_events_selected.fits.gz\r\n", "fermi_3fhl_exposure_cube_hpx.fits.gz\r\n", "fermi_3fhl_psf_gc.fits.gz\r\n", "gll_iem_v06_cutout.fits\r\n", "iso_P8R2_SOURCE_V6_v06.txt\r\n" ] } ], "source": [ "# Check that you have the prepared Fermi-LAT dataset\n", "# We will use diffuse models from here\n", "!ls -1 $GAMMAPY_DATA/fermi_3fhl" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:14.732080Z", "iopub.status.busy": "2021-11-22T21:10:14.731576Z", "iopub.status.idle": "2021-11-22T21:10:14.887965Z", "shell.execute_reply": "2021-11-22T21:10:14.888186Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:14.890593Z", "iopub.status.busy": "2021-11-22T21:10:14.890291Z", "iopub.status.idle": "2021-11-22T21:10:15.405847Z", "shell.execute_reply": "2021-11-22T21:10:15.406045Z" } }, "outputs": [], "source": [ "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "from gammapy.data import EventList\n", "from gammapy.datasets import MapDataset\n", "from gammapy.irf import PSFMap, EDispKernelMap\n", "from gammapy.maps import Map, MapAxis, WcsGeom\n", "from gammapy.modeling.models import (\n", " PowerLawSpectralModel,\n", " PointSpatialModel,\n", " SkyModel,\n", " TemplateSpatialModel,\n", " PowerLawNormSpectralModel,\n", " Models,\n", " create_fermi_isotropic_diffuse_model,\n", ")\n", "from gammapy.modeling import Fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Events\n", "\n", "To load up the Fermi-LAT event list, use the `~gammapy.data.EventList` class:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:15.408135Z", "iopub.status.busy": "2021-11-22T21:10:15.407858Z", "iopub.status.idle": "2021-11-22T21:10:16.162343Z", "shell.execute_reply": "2021-11-22T21:10:16.162582Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EventList\n", "---------\n", "\n", " Instrument : LAT\n", " Telescope : GLAST\n", " Obs. ID : \n", "\n", " Number of events : 697317\n", " Event rate : 0.003 1 / s\n", "\n", " Time start : 54682.65603222222\n", " Time stop : 57236.96833546296\n", "\n", " Min. energy : 1.00e+04 MeV\n", " Max. energy : 2.00e+06 MeV\n", " Median energy : 1.59e+04 MeV\n", "\n", "\n" ] } ], "source": [ "events = EventList.read(\n", " \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_events_selected.fits.gz\"\n", ")\n", "print(events)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The event data is stored in a [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html) object. In case of the Fermi-LAT event list this contains all the additional information on position, zenith angle, earth azimuth angle, event class, event type etc." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.165521Z", "iopub.status.busy": "2021-11-22T21:10:16.165181Z", "iopub.status.idle": "2021-11-22T21:10:16.166963Z", "shell.execute_reply": "2021-11-22T21:10:16.167145Z" } }, "outputs": [ { "data": { "text/plain": [ "['ENERGY',\n", " 'RA',\n", " 'DEC',\n", " 'L',\n", " 'B',\n", " 'THETA',\n", " 'PHI',\n", " 'ZENITH_ANGLE',\n", " 'EARTH_AZIMUTH_ANGLE',\n", " 'TIME',\n", " 'EVENT_ID',\n", " 'RUN_ID',\n", " 'RECON_VERSION',\n", " 'CALIB_VERSION',\n", " 'EVENT_CLASS',\n", " 'EVENT_TYPE',\n", " 'CONVERSION_TYPE',\n", " 'LIVETIME',\n", " 'DIFRSP0',\n", " 'DIFRSP1',\n", " 'DIFRSP2',\n", " 'DIFRSP3',\n", " 'DIFRSP4']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.table.colnames" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.169796Z", "iopub.status.busy": "2021-11-22T21:10:16.169437Z", "iopub.status.idle": "2021-11-22T21:10:16.170818Z", "shell.execute_reply": "2021-11-22T21:10:16.170994Z" } }, "outputs": [ { "data": { "text/html": [ "
Table length=5\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
ENERGYRADEC
MeVdegdeg
float32float32float32
12856.5205139.64438-9.93702
14773.319177.0445460.55275
23273.527110.2132537.002018
41866.125334.8528717.577398
42463.074316.8667648.152477
" ], "text/plain": [ "\n", " ENERGY RA DEC \n", " MeV deg deg \n", " float32 float32 float32 \n", "---------- --------- ---------\n", "12856.5205 139.64438 -9.93702\n", " 14773.319 177.04454 60.55275\n", " 23273.527 110.21325 37.002018\n", " 41866.125 334.85287 17.577398\n", " 42463.074 316.86676 48.152477" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.table[:5][[\"ENERGY\", \"RA\", \"DEC\"]]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.173062Z", "iopub.status.busy": "2021-11-22T21:10:16.172601Z", "iopub.status.idle": "2021-11-22T21:10:16.320754Z", "shell.execute_reply": "2021-11-22T21:10:16.321000Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2008-08-04 15:49:26.782\n", "2015-07-30 11:00:41.226\n" ] } ], "source": [ "print(events.time[0].iso)\n", "print(events.time[-1].iso)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.323351Z", "iopub.status.busy": "2021-11-22T21:10:16.322875Z", "iopub.status.idle": "2021-11-22T21:10:16.346586Z", "shell.execute_reply": "2021-11-22T21:10:16.346761Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name = ENERGY\n", "mean = 28905.451171875 MeV\n", "std = 61051.7421875 MeV\n", "min = 10000.03125 MeV\n", "max = 1998482.75 MeV\n", "n_bad = 0\n", "length = 697317\n" ] } ], "source": [ "energy = events.energy\n", "energy.info(\"stats\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a short analysis example we will count the number of events above a certain minimum energy: " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.349243Z", "iopub.status.busy": "2021-11-22T21:10:16.348758Z", "iopub.status.idle": "2021-11-22T21:10:16.357825Z", "shell.execute_reply": "2021-11-22T21:10:16.358068Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Events above 10 GeV: 697317\n", "Events above 100 GeV: 23628\n", "Events above 1000 GeV: 544\n" ] } ], "source": [ "for e_min in [10, 100, 1000] * u.GeV:\n", " n = (events.energy > e_min).sum()\n", " print(f\"Events above {e_min:4.0f}: {n:5.0f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Counts\n", "\n", "Let us start to prepare things for an 3D map analysis of the Galactic center region with Gammapy. The first thing we do is to define the map geometry. We chose a TAN projection centered on position ``(glon, glat) = (0, 0)`` with pixel size 0.1 deg, and four energy bins." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.362435Z", "iopub.status.busy": "2021-11-22T21:10:16.361919Z", "iopub.status.idle": "2021-11-22T21:10:16.652685Z", "shell.execute_reply": "2021-11-22T21:10:16.652867Z" } }, "outputs": [], "source": [ "gc_pos = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n", "energy_axis = MapAxis.from_edges(\n", " [1e4, 3e4, 1e5, 3e5, 2e6], name=\"energy\", unit=\"MeV\", interp=\"log\"\n", ")\n", "counts = Map.create(\n", " skydir=gc_pos,\n", " npix=(100, 80),\n", " proj=\"TAN\",\n", " frame=\"galactic\",\n", " binsz=0.1,\n", " axes=[energy_axis],\n", " dtype=float,\n", ")\n", "# We put this call into the same Jupyter cell as the Map.create\n", "# because otherwise we could accidentally fill the counts\n", "# multiple times when executing the ``fill_by_coord`` multiple times.\n", "counts.fill_events(events)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.655055Z", "iopub.status.busy": "2021-11-22T21:10:16.654735Z", "iopub.status.idle": "2021-11-22T21:10:16.656178Z", "shell.execute_reply": "2021-11-22T21:10:16.656388Z" } }, "outputs": [ { "data": { "text/plain": [ "MapAxis\n", "\n", "\tname : energy \n", "\tunit : 'MeV' \n", "\tnbins : 4 \n", "\tnode type : edges \n", "\tedges min : 1.0e+04 MeV\n", "\tedges max : 2.0e+06 MeV\n", "\tinterp : log " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts.geom.axes[0]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.658327Z", "iopub.status.busy": "2021-11-22T21:10:16.658020Z", "iopub.status.idle": "2021-11-22T21:10:16.769424Z", "shell.execute_reply": "2021-11-22T21:10:16.769623Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/mambaforge/envs/gammapy-dev/lib/python3.9/site-packages/astropy/visualization/wcsaxes/core.py:211: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n", " return super().imshow(X, *args, origin=origin, **kwargs)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "counts.sum_over_axes().smooth(2).plot(stretch=\"sqrt\", vmax=30);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exposure\n", "\n", "The Fermi-LAT dataset contains the energy-dependent exposure for the whole sky as a HEALPix map computed with ``gtexpcube2``. This format is supported by `~gammapy.maps` directly.\n", "\n", "Interpolating the exposure cube from the Fermi ST to get an exposure cube matching the spatial geometry and energy axis defined above with Gammapy is easy. The only point to watch out for is how exactly you want the energy axis and binning handled.\n", "\n", "Below we just use the default behaviour, which is linear interpolation in energy on the original exposure cube. Probably log interpolation would be better, but it doesn't matter much here, because the energy binning is fine. Finally, we just copy the counts map geometry, which contains an energy axis with `node_type=\"edges\"`. This is non-ideal for exposure cubes, but again, acceptable because exposure doesn't vary much from bin to bin, so the exact way interpolation occurs in later use of that exposure cube doesn't matter a lot. Of course you could define any energy axis for your exposure cube that you like." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.771833Z", "iopub.status.busy": "2021-11-22T21:10:16.771486Z", "iopub.status.idle": "2021-11-22T21:10:16.924970Z", "shell.execute_reply": "2021-11-22T21:10:16.925168Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HpxGeom\n", "\n", "\taxes : ['skycoord', 'energy_true']\n", "\tshape : (49152, 18)\n", "\tndim : 3\n", "\tnside : 64\n", "\tnested : False\n", "\tframe : icrs\n", "\tprojection : HPX\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\n", "MapAxis\n", "\n", "\tname : energy_true\n", "\tunit : 'MeV' \n", "\tnbins : 18 \n", "\tnode type : center \n", "\tcenter min : 1.0e+04 MeV\n", "\tcenter max : 2.0e+06 MeV\n", "\tinterp : log \n", "\n" ] } ], "source": [ "exposure_hpx = Map.read(\n", " \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz\"\n", ")\n", "print(exposure_hpx.geom)\n", "print(exposure_hpx.geom.axes[0])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:16.926548Z", "iopub.status.busy": "2021-11-22T21:10:16.926237Z", "iopub.status.idle": "2021-11-22T21:10:17.304935Z", "shell.execute_reply": "2021-11-22T21:10:17.305185Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "exposure_hpx.plot();" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.307800Z", "iopub.status.busy": "2021-11-22T21:10:17.307491Z", "iopub.status.idle": "2021-11-22T21:10:17.429532Z", "shell.execute_reply": "2021-11-22T21:10:17.429744Z" } }, "outputs": [], "source": [ "# For exposure, we choose a geometry with node_type='center',\n", "# whereas for counts it was node_type='edge'\n", "axis = MapAxis.from_energy_bounds(\n", " \"10 GeV\",\n", " \"2 TeV\",\n", " nbin=10,\n", " per_decade=True,\n", " name=\"energy_true\",\n", ")\n", "geom = WcsGeom(wcs=counts.geom.wcs, npix=counts.geom.npix, axes=[axis])\n", "\n", "exposure = exposure_hpx.interp_to_geom(geom)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.432227Z", "iopub.status.busy": "2021-11-22T21:10:17.431865Z", "iopub.status.idle": "2021-11-22T21:10:17.433445Z", "shell.execute_reply": "2021-11-22T21:10:17.433652Z" } }, "outputs": [ { "data": { "text/plain": [ "MapAxis\n", "\n", "\tname : energy \n", "\tunit : 'MeV' \n", "\tnbins : 4 \n", "\tnode type : edges \n", "\tedges min : 1.0e+04 MeV\n", "\tedges max : 2.0e+06 MeV\n", "\tinterp : log " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts.geom.axes[0]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.436220Z", "iopub.status.busy": "2021-11-22T21:10:17.435851Z", "iopub.status.idle": "2021-11-22T21:10:17.437636Z", "shell.execute_reply": "2021-11-22T21:10:17.437866Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : ['lon', 'lat', 'energy_true']\n", "\tshape : (100, 80, 24)\n", "\tndim : 3\n", "\tframe : galactic\n", "\tprojection : TAN\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 deg x 8.0 deg\n", "\twcs ref : 0.0 deg, 0.0 deg\n", "\n", "MapAxis\n", "\n", "\tname : energy_true\n", "\tunit : 'TeV' \n", "\tnbins : 24 \n", "\tnode type : edges \n", "\tedges min : 1.0e-02 TeV\n", "\tedges max : 2.0e+00 TeV\n", "\tinterp : log \n", "\n" ] } ], "source": [ "print(exposure.geom)\n", "print(exposure.geom.axes[0])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.447513Z", "iopub.status.busy": "2021-11-22T21:10:17.447161Z", "iopub.status.idle": "2021-11-22T21:10:17.519942Z", "shell.execute_reply": "2021-11-22T21:10:17.520180Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Exposure is almost constant across the field of view\n", "exposure.slice_by_idx({\"energy_true\": 0}).plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.523078Z", "iopub.status.busy": "2021-11-22T21:10:17.522702Z", "iopub.status.idle": "2021-11-22T21:10:17.524265Z", "shell.execute_reply": "2021-11-22T21:10:17.524581Z" } }, "outputs": [ { "data": { "text/plain": [ "array([3.22974080e+11, 3.29585273e+11, 2.90605275e+11])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Exposure varies very little with energy at these high energies\n", "energy = [10, 100, 1000] * u.GeV\n", "exposure.get_by_coord({\"skycoord\": gc_pos, \"energy_true\": energy})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Galactic diffuse background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Fermi-LAT collaboration provides a galactic diffuse emission model, that can be used as a background model for\n", "Fermi-LAT source analysis.\n", "\n", "Diffuse model maps are very large (100s of MB), so as an example here, we just load one that represents a small cutout for the Galactic center region." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.526619Z", "iopub.status.busy": "2021-11-22T21:10:17.526286Z", "iopub.status.idle": "2021-11-22T21:10:17.547562Z", "shell.execute_reply": "2021-11-22T21:10:17.547747Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : ['lon', 'lat', 'energy_true']\n", "\tshape : (120, 64, 30)\n", "\tndim : 3\n", "\tframe : galactic\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, -0.1 deg\n", "\twidth : 15.0 deg x 8.0 deg\n", "\twcs ref : 0.0 deg, 0.0 deg\n", "\n", "MapAxis\n", "\n", "\tname : energy_true\n", "\tunit : 'MeV' \n", "\tnbins : 30 \n", "\tnode type : center \n", "\tcenter min : 5.8e+01 MeV\n", "\tcenter max : 5.1e+05 MeV\n", "\tinterp : log \n", "\n" ] } ], "source": [ "diffuse_galactic_fermi = Map.read(\n", " \"$GAMMAPY_DATA/fermi-3fhl-gc/gll_iem_v06_gc.fits.gz\"\n", ")\n", "\n", "# Unit is not stored in the file, set it manually\n", "diffuse_galactic_fermi.unit = \"cm-2 s-1 MeV-1 sr-1\"\n", "print(diffuse_galactic_fermi.geom)\n", "\n", "print(diffuse_galactic_fermi.geom.axes[0])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.549467Z", "iopub.status.busy": "2021-11-22T21:10:17.549143Z", "iopub.status.idle": "2021-11-22T21:10:17.551177Z", "shell.execute_reply": "2021-11-22T21:10:17.551366Z" } }, "outputs": [], "source": [ "template_diffuse = TemplateSpatialModel(\n", " diffuse_galactic_fermi, normalize=False\n", ")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.555800Z", "iopub.status.busy": "2021-11-22T21:10:17.552412Z", "iopub.status.idle": "2021-11-22T21:10:17.563074Z", "shell.execute_reply": "2021-11-22T21:10:17.563307Z" } }, "outputs": [], "source": [ "diffuse_iem = SkyModel(\n", " spectral_model=PowerLawNormSpectralModel(),\n", " spatial_model=template_diffuse,\n", " name=\"diffuse-iem\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the map of first energy band of the cube:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.565462Z", "iopub.status.busy": "2021-11-22T21:10:17.565154Z", "iopub.status.idle": "2021-11-22T21:10:17.633714Z", "shell.execute_reply": "2021-11-22T21:10:17.633958Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "template_diffuse.map.slice_by_idx({\"energy_true\": 0}).plot(add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the spectrum at the Glaactic center:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.636088Z", "iopub.status.busy": "2021-11-22T21:10:17.635777Z", "iopub.status.idle": "2021-11-22T21:10:17.798439Z", "shell.execute_reply": "2021-11-22T21:10:17.798722Z" } }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dnde = template_diffuse.map.to_region_nd_map(region=gc_pos)\n", "dnde.plot()\n", "plt.xlabel(\"Energy (GeV)\")\n", "plt.ylabel(\"Flux (cm-2 s-1 MeV-1 sr-1)\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Isotropic diffuse background\n", "\n", "To load the isotropic diffuse model with Gammapy, use the `~gammapy.modeling.models.TemplateSpectralModel`. We are using `'fill_value': 'extrapolate'` to extrapolate the model above 500 GeV:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.801071Z", "iopub.status.busy": "2021-11-22T21:10:17.800740Z", "iopub.status.idle": "2021-11-22T21:10:17.805059Z", "shell.execute_reply": "2021-11-22T21:10:17.805293Z" } }, "outputs": [], "source": [ "filename = \"$GAMMAPY_DATA/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt\"\n", "\n", "diffuse_iso = create_fermi_isotropic_diffuse_model(\n", " filename=filename, interp_kwargs={\"fill_value\": None}\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the model in the energy range between 50 GeV and 2000 GeV:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.807689Z", "iopub.status.busy": "2021-11-22T21:10:17.807333Z", "iopub.status.idle": "2021-11-22T21:10:17.995345Z", "shell.execute_reply": "2021-11-22T21:10:17.995587Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "energy_bounds = [50, 2000] * u.GeV\n", "diffuse_iso.spectral_model.plot(\n", " energy_bounds, yunits=u.Unit(\"1 / (cm2 MeV s)\")\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PSF\n", "\n", "Next we will tke a look at the PSF. It was computed using ``gtpsf``, in this case for the Galactic center position. Note that generally for Fermi-LAT, the PSF only varies little within a given regions of the sky, especially at high energies like what we have here. We use the `~gammapy.irf.PSFMap` class to load the PSF and use some of it's methods to get some information about it." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:17.997648Z", "iopub.status.busy": "2021-11-22T21:10:17.997294Z", "iopub.status.idle": "2021-11-22T21:10:18.005149Z", "shell.execute_reply": "2021-11-22T21:10:18.005362Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RegionNDMap\n", "\n", "\tgeom : RegionGeom \n", " \taxes : ['lon', 'lat', 'rad', 'energy_true']\n", "\tshape : (1, 1, 300, 17)\n", "\tndim : 4\n", "\tunit : 1 / sr\n", "\tdtype : >f8\n", "\n" ] } ], "source": [ "psf = PSFMap.read(\n", " \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_psf_gc.fits.gz\", format=\"gtpsf\"\n", ")\n", "print(psf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get an idea of the size of the PSF we check how the containment radii of the Fermi-LAT PSF vari with energy and different containment fractions:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:18.007958Z", "iopub.status.busy": "2021-11-22T21:10:18.007615Z", "iopub.status.idle": "2021-11-22T21:10:18.199690Z", "shell.execute_reply": "2021-11-22T21:10:18.199887Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5))\n", "psf.plot_containment_radius_vs_energy()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition we can check how the actual shape of the PSF varies with energy and compare it against the mean PSF between 50 GeV and 2000 GeV:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:18.204779Z", "iopub.status.busy": "2021-11-22T21:10:18.204477Z", "iopub.status.idle": "2021-11-22T21:10:18.346122Z", "shell.execute_reply": "2021-11-22T21:10:18.346330Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5))\n", "\n", "energy = [100, 300, 1000] * u.GeV\n", "psf.plot_psf_vs_rad(energy_true=energy)\n", "\n", "spectrum = PowerLawSpectralModel(index=2.3)\n", "psf_mean = psf.to_image(spectrum=spectrum)\n", "psf_mean.plot_psf_vs_rad(c=\"k\", ls=\"--\", energy_true=[500] * u.GeV)\n", "\n", "plt.xlim(1e-3, 0.3)\n", "plt.ylim(1e3, 1e6)\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:18.348674Z", "iopub.status.busy": "2021-11-22T21:10:18.348314Z", "iopub.status.idle": "2021-11-22T21:10:18.461980Z", "shell.execute_reply": "2021-11-22T21:10:18.462177Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "psf_kernel = psf.get_psf_kernel(\n", " position=geom.center_skydir, geom=geom, max_radius=\"1 deg\"\n", ")\n", "psf_kernel.to_image().psf_kernel_map.plot(stretch=\"log\", add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Energy Dispersion\n", "For simplicity we assume a diagonal energy dispersion:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:18.466213Z", "iopub.status.busy": "2021-11-22T21:10:18.465911Z", "iopub.status.idle": "2021-11-22T21:10:18.467041Z", "shell.execute_reply": "2021-11-22T21:10:18.467293Z" } }, "outputs": [], "source": [ "e_true = exposure.geom.axes[\"energy_true\"]\n", "edisp = EDispKernelMap.from_diagonal_response(\n", " energy_axis_true=e_true, energy_axis=energy_axis\n", ")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:18.482431Z", "iopub.status.busy": "2021-11-22T21:10:18.482029Z", "iopub.status.idle": "2021-11-22T21:10:18.636450Z", "shell.execute_reply": "2021-11-22T21:10:18.636655Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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inSnsAY4Ymj4c+NScatFisg1p2jZcm1qkUJjmmEnaP9mGNG0brk2ty1Doxky6EnhEkj1JTq+qu4EXAJcDHwMuqqqPzrNOrV+2IU3b/tKmHPtIktSsyzMFSdJ8GAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKGhhJLknyYeHXmOfhzBLSQ5N8vwe9392kk8m+fUkzx76GdyV5Nru/WuX2e7dSZ60ZN6Lk5yT5GHddnf0VbcWjzevaWEkuaOqDp7yPjd3d6Xu636OBC6tqkcvs2xTVe3TmPpJzgbuqKrfXjL/E8DWqrppxHbPAb6zqp49NO+fgDOr6r3d9NR/rlpcnilo4SX5RJJfS/LP3V/Nj+zm3797WtbVSf4lySnd/GcleWuSncA7knxNkouS7EryF0muSrI1yelJfnfoOGckef2IMl4L7P3L+3VJHp/kXUnOB65NcuTwE7uSvLT7RU/3F/vfJflQkvfurX8NP4czu8+6K8mvdbMvBp6S5IBunSOBbwbet5ZjaOMzFLRIDlry9dHwY1ZvqqrHAW8EXtrNeznwj1X17cAPAK9Lcv9u2XcBz6yqHwSeD9xSVccCrwJO6Na5EHhqkvt2088G/mxEbWcB/1FVx1XVmd28E4GXV9UxK3yuHcALq+qErvZzVlj/XjJ4DvmW7pjHASck+b5u+O8P8tUnhp0K/EX5FYFGWKTnKUhfqKrjRiy7pPv3QwyejwvwRAa/1PeGxIHAQ7v3f19VN3fvvxf4fYCq2p1kV/f+ziT/yOAv7Y8B962qa1dR7wer6j/HrZDkYOC7gbcOHhYIwAGrOMZeT+xe/9JNH8wgJK4ALmAQBn/d/fuza9i/9hOGgjaKL3X/3sNX23WAH6uq64ZXTPIdwJ3Ds8bs983Ay4CPM/osYZThY9zN/z8zP7D79z7ArWPCblIBfqOq/niZZW8DXp/kccBBPp5W4/j1kTayy4EXds/rJsnxI9Z7H/CT3TrHAI/Zu6CqrmLwEJWfYvAX9yj/CxwyZvmngYd0j/g8gO6Z0FV1O/CfSX6iO36SPHaCz7bU5cDPdmceJDksyUO6Y9wBvBs4d4XPIBkKWihL+xTudQnmEq8C7gvs6jp5XzVivXOAB3dfG/0SsAu4bWj5RcD7q+qWUQfqvrt/f5LdSV63zPIvA78OXAVcyuDMY69nAKcn+QjwUQYPfl+VqnoHcD5wZZJrGXQwD4fUBcBjGfSTSCN5Sar2e0k2Megv+GKShwHvBB5eVXd1yy8Ffreq3jnHGs9mmUtSp7RvL0lVY5+CBF8DvKu7yijA86rqriSHMrhy5yPzDITOHcD2JF9bVb86jR12AfiXDL7akgDPFKRVSfIgBmcSSz2h+wpJWmiGgiSpsaNZktQYCpKkxlCQJDWGgiSpMRQkSc3/AeMVRn9U/UTXAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "edisp.get_edisp_kernel().plot_matrix()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit\n", "Now, the big finale: let’s do a 3D map fit for the source at the Galactic center, to measure it’s position and spectrum. We keep the background normalization free." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:18.643042Z", "iopub.status.busy": "2021-11-22T21:10:18.641939Z", "iopub.status.idle": "2021-11-22T21:10:18.648982Z", "shell.execute_reply": "2021-11-22T21:10:18.649151Z" } }, "outputs": [], "source": [ "spatial_model = PointSpatialModel(\n", " lon_0=\"0 deg\", lat_0=\"0 deg\", frame=\"galactic\"\n", ")\n", "spectral_model = PowerLawSpectralModel(\n", " index=2.7, amplitude=\"5.8e-10 cm-2 s-1 TeV-1\", reference=\"100 GeV\"\n", ")\n", "\n", "source = SkyModel(\n", " spectral_model=spectral_model,\n", " spatial_model=spatial_model,\n", " name=\"source-gc\",\n", ")\n", "\n", "models = Models([source, diffuse_iem, diffuse_iso])\n", "\n", "dataset = MapDataset(\n", " models=models, counts=counts, exposure=exposure, psf=psf, edisp=edisp\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:18.651541Z", "iopub.status.busy": "2021-11-22T21:10:18.651241Z", "iopub.status.idle": "2021-11-22T21:10:20.209582Z", "shell.execute_reply": "2021-11-22T21:10:20.209812Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.5 s, sys: 120 ms, total: 1.62 s\n", "Wall time: 1.56 s\n" ] } ], "source": [ "%%time\n", "fit = Fit()\n", "result = fit.run(datasets=[dataset])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:20.211682Z", "iopub.status.busy": "2021-11-22T21:10:20.211384Z", "iopub.status.idle": "2021-11-22T21:10:20.212653Z", "shell.execute_reply": "2021-11-22T21:10:20.212877Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 192\n", "\ttotal stat : 19666.08\n", "\n", "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 192\n", "\ttotal stat : 19666.08\n", "\n", "\n" ] } ], "source": [ "print(result)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:20.214593Z", "iopub.status.busy": "2021-11-22T21:10:20.214300Z", "iopub.status.idle": "2021-11-22T21:10:20.215740Z", "shell.execute_reply": "2021-11-22T21:10:20.215896Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Models\n", "\n", "Component 0: SkyModel\n", "\n", " Name : source-gc\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : PointSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 2.764 +/- 0.11 \n", " amplitude : 5.20e-10 +/- 1.1e-10 1 / (cm2 s TeV)\n", " reference (frozen) : 100.000 GeV \n", " lon_0 : -0.025 +/- 0.00 deg \n", " lat_0 : -0.041 +/- 0.00 deg \n", "\n", "Component 1: SkyModel\n", "\n", " Name : diffuse-iem\n", " Datasets names : None\n", " Spectral model type : PowerLawNormSpectralModel\n", " Spatial model type : TemplateSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " norm : 0.975 +/- 0.01 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "Component 2: SkyModel\n", "\n", " Name : fermi-diffuse-iso\n", " Datasets names : None\n", " Spectral model type : CompoundSpectralModel\n", " Spatial model type : ConstantSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " norm : 3.123 +/- 0.38 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", " value (frozen) : 1.000 1 / sr \n", "\n", "\n" ] } ], "source": [ "print(models)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:10:20.218540Z", "iopub.status.busy": "2021-11-22T21:10:20.218243Z", "iopub.status.idle": "2021-11-22T21:10:20.302202Z", "shell.execute_reply": "2021-11-22T21:10:20.302390Z" }, "nbsphinx-thumbnail": { "tooltip": "Data inspection and preliminary analysis with Fermi-LAT data." } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/mambaforge/envs/gammapy-dev/lib/python3.9/site-packages/astropy/visualization/wcsaxes/core.py:211: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n", " return super().imshow(X, *args, origin=origin, **kwargs)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "residual = counts - dataset.npred()\n", "residual.sum_over_axes().smooth(\"0.1 deg\").plot(\n", " cmap=\"coolwarm\", vmin=-3, vmax=3, add_cbar=True\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "- Fit the position and spectrum of the source [SNR G0.9+0.1](http://gamma-sky.net/#/cat/tev/110).\n", "- Make maps and fit the position and spectrum of the [Crab nebula](http://gamma-sky.net/#/cat/tev/25)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "In this tutorial you have seen how to work with Fermi-LAT data with Gammapy. You have to use the Fermi ST to prepare the exposure cube and PSF, and then you can use Gammapy for any event or map analysis using the same methods that are used to analyse IACT data.\n", "\n", "This works very well at high energies (here above 10 GeV), where the exposure and PSF is almost constant spatially and only varies a little with energy. It is not expected to give good results for low-energy data, where the Fermi-LAT PSF is very large. If you are interested to help us validate down to what energy Fermi-LAT analysis with Gammapy works well (e.g. by re-computing results from 3FHL or other published analysis results), or to extend the Gammapy capabilities (e.g. to work with energy-dependent multi-resolution maps and PSF), that would be very welcome!" ] } ], "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": "3.9.0" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1.0, "eqLabelWithNumbers": true, "eqNumInitial": 1.0, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "nbsphinx": { "orphan": true }, "toc": { "base_numbering": 1.0, "nav_menu": { "height": "237px", "width": "253px" }, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16.0, "lenType": 16.0, "lenVar": 40.0 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }