{ "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/v0.18.2?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/docs/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 the `CorrelatedExcessMapEstimator`\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 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.makers import RingBackgroundMaker\n", "from gammapy.estimators import ExcessMapEstimator\n", "from gammapy.maps import Map\n", "from gammapy.datasets import MapDatasetOnOff" ] }, { "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:\n", " method: null\n", " exclusion: null\n", " parameters: {}\n", " safe_mask:\n", " methods: [aeff-default]\n", " parameters: {}\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", " parameters: {}\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 205 ms, sys: 2.98 ms, total: 208 ms\n", "Wall time: 208 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:\n", " method: null\n", " exclusion: null\n", " parameters: {}\n", " safe_mask:\n", " methods: [aeff-default]\n", " parameters: {}\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", " parameters: {}\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", "No background maker set for 3d analysis. Check configuration.\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 13.4 s, sys: 1.19 s, total: 14.6 s\n", "Wall time: 14.7 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.axes[\"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/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: invalid value encountered in true_divide\n", " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" ] } ], "source": [ "#%%time\n", "energy_axis_true = analysis.datasets[0].exposure.geom.axes[\"energy_true\"]\n", "stacked_on_off = MapDatasetOnOff.create(\n", " geom=geom_image, energy_axis_true=energy_axis_true, name=\"stacked\"\n", ")\n", "\n", "for dataset in analysis.datasets:\n", " # Ring extracting makes sense only for 2D analysis\n", " dataset_on_off = ring_maker.run(dataset.to_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 : stacked \n", "\n", " Total counts : 39183 \n", " Total background counts : 38320.57\n", " Total excess counts : 862.43\n", "\n", " Predicted counts : 38320.92\n", " Predicted background counts : 38320.92\n", " Predicted excess counts : nan\n", "\n", " Exposure min : 1.11e+09 m2 s\n", " Exposure max : 1.30e+10 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)) : 26399.35\n", "\n", " Number of models : 0 \n", " Number of parameters : 0\n", " Number of free parameters : 0\n", "\n", " Total counts_off : 86301192 \n", " Acceptance : 22500 \n", " Acceptance off : 49478073 \n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: divide by zero encountered in true_divide\n", " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\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": [ "# Using a convolution radius of 0.04 degrees\n", "estimator = ExcessMapEstimator(0.04 * u.deg, selection_optional=[])\n", "lima_maps = estimator.run(stacked_on_off)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "significance_map = lima_maps[\"sqrt_ts\"]\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.plot(ax=ax1, add_cbar=True)\n", "\n", "ax2.set_title(\"Excess map\")\n", "excess_map.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 }