{ "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/v0.16?urlpath=lab/tree/ring_background.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n", "- **Source files:**\n", "[ring_background.ipynb](../_static/notebooks/ring_background.ipynb) |\n", "[ring_background.py](../_static/notebooks/ring_background.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ring Background Estimation\n", "\n", "## Context:\n", "One of the challenges of IACT analysis is accounting for the large residual hadronic emission. An excess map, assumed to be a map of only gamma-ray events, requires a good estimate of the background. However, in the absence of a solid template bkg model it is not possible to obtain reliable background model a priori. It was often found necessary in classical cherenkov astronomy to perform a local renormalization of the existing templates, usually with a ring kernel. This assumes that most of the events are background and requires to have an exclusion mask to remove regions with bright signal from the estimation. To read more about this method, see [here.](https://arxiv.org/abs/astro-ph/0610959)\n", "\n", "## Objective:\n", "Create an excess (gamma-ray events) map of MSH 15-52 as well as a significance map to determine how solid the signal is.\n", "\n", "## Proposed approach:\n", "\n", "The analysis workflow is roughly\n", " - Compute the sky maps keeping each observation separately using the `Analysis` class\n", " - Estimate the background using the `RingBackgroundMaker`\n", " - Compute the correlated excess and significance maps using `compute_lima_on_off_image`\n", " \n", "The normalised background thus obtained can be used for general modelling and fitting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "As usual, we'll start with some general imports..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from regions import CircleSkyRegion\n", "from astropy.convolution import Tophat2DKernel\n", "from scipy.stats import norm\n", "\n", "import logging\n", "\n", "log = logging.getLogger(__name__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's import gammapy specific classes and functions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from gammapy.analysis import Analysis, AnalysisConfig\n", "from gammapy.cube import RingBackgroundMaker, MapDatasetOnOff\n", "from gammapy.detect import compute_lima_on_off_image\n", "from gammapy.maps import Map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating the config file\n", "Now, we create a config file for out analysis. You may load this from disc if you have a pre-defined config file.\n", "\n", "In this example, we will use a few HESS runs on the pulsar wind nebula, MSH 1552" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# source_pos = SkyCoord.from_name(\"MSH 15-52\")\n", "source_pos = SkyCoord(228.32, -59.08, unit=\"deg\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "config = AnalysisConfig()\n", "# Select observations - 2.5 degrees from the source position\n", "config.observations.datastore = \"$GAMMAPY_DATA/hess-dl3-dr1/\"\n", "config.observations.obs_cone = {\n", " \"frame\": \"icrs\",\n", " \"lon\": source_pos.ra,\n", " \"lat\": source_pos.dec,\n", " \"radius\": 2.5 * u.deg,\n", "}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "config.datasets.type = \"3d\"\n", "config.datasets.geom.wcs.skydir = {\n", " \"lon\": source_pos.ra,\n", " \"lat\": source_pos.dec,\n", " \"frame\": \"icrs\",\n", "} # The WCS geometry - centered on MSH 15-52\n", "config.datasets.geom.wcs.fov = {\"width\": \"3 deg\", \"height\": \"3 deg\"}\n", "config.datasets.geom.wcs.binsize = \"0.02 deg\"\n", "\n", "# The FoV radius to use for cutouts\n", "config.datasets.geom.selection.offset_max = 3.5 * u.deg\n", "\n", "# We now fix the energy axis for the counts map - (the reconstructed energy binning)\n", "config.datasets.geom.axes.energy.min = \"0.5 TeV\"\n", "config.datasets.geom.axes.energy.max = \"5 TeV\"\n", "config.datasets.geom.axes.energy.nbins = 10\n", "\n", "# We need to extract the ring for each observation separately, hence, no stacking at this stage\n", "config.datasets.stack = False" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AnalysisConfig\n", "\n", " general:\n", " log: {level: info, filename: null, filemode: null, format: null, datefmt: null}\n", " outdir: .\n", " observations:\n", " datastore: $GAMMAPY_DATA/hess-dl3-dr1\n", " obs_ids: []\n", " obs_file: null\n", " obs_cone: {frame: icrs, lon: 228.32 deg, lat: -59.08 deg, radius: 2.5 deg}\n", " obs_time: {start: null, stop: null}\n", " datasets:\n", " type: 3d\n", " stack: false\n", " geom:\n", " wcs:\n", " skydir: {frame: icrs, lon: 228.32 deg, lat: -59.08 deg}\n", " binsize: 0.02 deg\n", " fov: {width: 3.0 deg, height: 3.0 deg}\n", " binsize_irf: 0.2 deg\n", " selection: {offset_max: 3.5 deg}\n", " axes:\n", " energy: {min: 0.5 TeV, max: 5.0 TeV, nbins: 10}\n", " energy_true: {min: 0.1 TeV, max: 10.0 TeV, nbins: 30}\n", " map_selection: [counts, exposure, background, psf, edisp]\n", " background: {method: reflected, exclusion: null}\n", " safe_mask:\n", " methods: [aeff-default]\n", " settings: {}\n", " on_region: {frame: null, lon: null, lat: null, radius: null}\n", " containment_correction: true\n", " fit:\n", " fit_range: {min: 0.1 TeV, max: 10.0 TeV}\n", " flux_points:\n", " energy: {min: 0.1 TeV, max: 10.0 TeV, nbins: 30}\n", " source: source\n", " params: {}\n", " \n" ] } ], "source": [ "print(config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting the reduced dataset\n", "We now use the config file to do the initial data reduction which will then be used for a ring extraction" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Setting logging config: {'level': 'INFO', 'filename': None, 'filemode': None, 'format': None, 'datefmt': None}\n", "Fetching observations.\n", "Number of selected observations: 20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 44.8 ms, sys: 1.69 ms, total: 46.5 ms\n", "Wall time: 46.5 ms\n" ] } ], "source": [ "%%time\n", "# create the config\n", "analysis = Analysis(config)\n", "\n", "# for this specific case,w e do not need fine bins in true energy\n", "analysis.config.datasets.geom.axes.energy_true = (\n", " analysis.config.datasets.geom.axes.energy\n", ")\n", "\n", "# `First get the required observations\n", "analysis.get_observations()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AnalysisConfig\n", "\n", " general:\n", " log: {level: INFO, filename: null, filemode: null, format: null, datefmt: null}\n", " outdir: .\n", " observations:\n", " datastore: $GAMMAPY_DATA/hess-dl3-dr1\n", " obs_ids: []\n", " obs_file: null\n", " obs_cone: {frame: icrs, lon: 228.32 deg, lat: -59.08 deg, radius: 2.5 deg}\n", " obs_time: {start: null, stop: null}\n", " datasets:\n", " type: 3d\n", " stack: false\n", " geom:\n", " wcs:\n", " skydir: {frame: icrs, lon: 228.32 deg, lat: -59.08 deg}\n", " binsize: 0.02 deg\n", " fov: {width: 3.0 deg, height: 3.0 deg}\n", " binsize_irf: 0.2 deg\n", " selection: {offset_max: 3.5 deg}\n", " axes:\n", " energy: {min: 0.5 TeV, max: 5.0 TeV, nbins: 10}\n", " energy_true: {min: 0.5 TeV, max: 5.0 TeV, nbins: 10}\n", " map_selection: [counts, exposure, background, psf, edisp]\n", " background: {method: reflected, exclusion: null}\n", " safe_mask:\n", " methods: [aeff-default]\n", " settings: {}\n", " on_region: {frame: null, lon: null, lat: null, radius: null}\n", " containment_correction: true\n", " fit:\n", " fit_range: {min: 0.1 TeV, max: 10.0 TeV}\n", " flux_points:\n", " energy: {min: 0.1 TeV, max: 10.0 TeV, nbins: 30}\n", " source: source\n", " params: {}\n", " \n" ] } ], "source": [ "print(analysis.config)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Creating geometry.\n", "Creating datasets.\n", "Processing observation 20365\n", "Processing observation 20366\n", "Processing observation 20367\n", "Processing observation 20368\n", "Processing observation 20136\n", "Processing observation 20137\n", "Processing observation 20151\n", "Processing observation 20282\n", "Processing observation 20283\n", "Processing observation 20301\n", "Processing observation 20302\n", "Processing observation 20303\n", "Processing observation 20322\n", "Processing observation 20323\n", "Processing observation 20324\n", "Processing observation 20325\n", "Processing observation 20343\n", "Processing observation 20344\n", "Processing observation 20345\n", "Processing observation 20346\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 12.1 s, sys: 1.19 s, total: 13.3 s\n", "Wall time: 13.6 s\n" ] } ], "source": [ "%%time\n", "# Data extraction\n", "analysis.get_datasets()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extracting the ring background\n", "\n", "Since the ring background is extracted from real off events, we need to use the wstat statistics in this case. For this, we will use the `MapDatasetOnOFF` and the `RingBackgroundMaker` classes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create exclusion mask\n", "First, we need to create an exclusion mask on the known sources. In this case, we need to mask only `MSH 15-52` but this depends on the sources present in our field of view." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# get the geom that we use\n", "geom = analysis.datasets[0].counts.geom\n", "energy_axis = analysis.datasets[0].counts.geom.get_axis_by_name(\"energy\")\n", "geom_image = geom.to_image().to_cube([energy_axis.squash()])\n", "\n", "# Make the exclusion mask\n", "regions = CircleSkyRegion(center=source_pos, radius=0.3 * u.deg)\n", "exclusion_mask = Map.from_geom(geom_image)\n", "exclusion_mask.data = geom_image.region_mask([regions], inside=False)\n", "exclusion_mask.sum_over_axes().plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the present analysis, we use a ring with an inner radius of 0.5 deg and width of 0.3 deg." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "ring_maker = RingBackgroundMaker(\n", " r_in=\"0.5 deg\", width=\"0.3 deg\", exclusion_mask=exclusion_mask\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a stacked dataset\n", "Now, we extract the background for each dataset and then stack the maps together to create a single stacked map for further analysis" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/github/adonath/astropy/astropy/units/quantity.py:481: RuntimeWarning: invalid value encountered in true_divide\n", " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" ] } ], "source": [ "#%%time\n", "stacked_on_off = MapDatasetOnOff.create(geom=geom_image)\n", "for dataset in analysis.datasets:\n", " dataset_image = (\n", " dataset.to_image()\n", " ) # Ring extracting makes sense only for 2D analysis\n", " dataset_on_off = ring_maker.run(dataset_image)\n", " stacked_on_off.stack(dataset_on_off)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This `stacked_on_off` has `on` and `off` counts and acceptance maps which we will use in all further analysis. The `acceptance` and `acceptance_off` maps are the system acceptance of gamma-ray like events in the `on` and `off` regions respectively." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapDatasetOnOff\n", "---------------\n", "\n", " Name : C8nrQdgz \n", "\n", " Total counts : 40051 \n", " Total predicted counts : nan\n", " Total background counts : nan\n", "\n", " Exposure min : 2.34e+09 m2 s\n", " Exposure max : 8.18e+09 m2 s\n", "\n", " Number of total bins : 22500 \n", " Number of fit bins : 22500 \n", "\n", " Fit statistic type : wstat\n", " Fit statistic value (-2 log(L)) : nan\n", "\n", " Number of models : 0 \n", " Number of parameters : 0\n", " Number of free parameters : 0\n", "\n", " Total counts_off : 88113384 \n", " Acceptance : 22500 \n", " Acceptance off : 49447599 \n", "\n" ] } ], "source": [ "print(stacked_on_off)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute correlated significance and correlated excess maps\n", "We need to convolve our maps with an apprpriate smoothing kernel. The significance is computed according to the Li & Ma expression for ON and OFF Poisson measurements, see [here](https://ui.adsabs.harvard.edu/abs/1983ApJ...272..317L/abstract). Since astropy convolution kernels only accept integers, we first convert our required size in degrees to int depending on our pixel size." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "scale = geom.pixel_scales[0].to(\"deg\")\n", "# Using a convolution radius of 0.04 degrees\n", "theta = 0.04 * u.deg / scale\n", "tophat = Tophat2DKernel(theta)\n", "tophat.normalize(\"peak\")\n", "\n", "lima_maps = compute_lima_on_off_image(\n", " stacked_on_off.counts,\n", " stacked_on_off.counts_off,\n", " stacked_on_off.acceptance,\n", " stacked_on_off.acceptance_off,\n", " tophat,\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "significance_map = lima_maps[\"significance\"]\n", "excess_map = lima_maps[\"excess\"]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " ,\n", " )" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We can plot the excess and significance maps\n", "plt.figure(figsize=(10, 10))\n", "ax1 = plt.subplot(221, projection=significance_map.geom.wcs)\n", "ax2 = plt.subplot(222, projection=excess_map.geom.wcs)\n", "\n", "ax1.set_title(\"Significance map\")\n", "significance_map.get_image_by_idx((0,)).plot(ax=ax1, add_cbar=True)\n", "\n", "ax2.set_title(\"Excess map\")\n", "excess_map.get_image_by_idx((0,)).plot(ax=ax2, add_cbar=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is often important to look at the signficance distribution outside the exclusion region to check that the background estimation is not contaminated by gamma-ray events. This can be the case when exclusion regions are not large enough.\n", "Typically, we expect the off distribution to be a standard normal distribution." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fit results: mu = -0.02, std = 1.00\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create a 2D mask for the images\n", "significance_map_off = significance_map * exclusion_mask\n", "significance_all = significance_map.data[np.isfinite(significance_map.data)]\n", "significance_off = significance_map_off.data[\n", " np.isfinite(significance_map_off.data)\n", "]\n", "\n", "plt.hist(\n", " significance_all,\n", " density=True,\n", " alpha=0.5,\n", " color=\"red\",\n", " label=\"all bins\",\n", " bins=21,\n", ")\n", "\n", "plt.hist(\n", " significance_off,\n", " density=True,\n", " alpha=0.5,\n", " color=\"blue\",\n", " label=\"off bins\",\n", " bins=21,\n", ")\n", "\n", "# Now, fit the off distribution with a Gaussian\n", "mu, std = norm.fit(significance_off)\n", "x = np.linspace(-8, 8, 50)\n", "p = norm.pdf(x, mu, std)\n", "plt.plot(x, p, lw=2, color=\"black\")\n", "plt.legend()\n", "plt.xlabel(\"Significance\")\n", "plt.yscale(\"log\")\n", "plt.ylim(1e-5, 1)\n", "xmin, xmax = np.min(significance_all), np.max(significance_all)\n", "plt.xlim(xmin, xmax)\n", "\n", "print(f\"Fit results: mu = {mu:.2f}, std = {std:.2f}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }