\n",
"\n",
"**This is a fixed-text formatted version of a Jupyter notebook**\n",
"\n",
"- Try online [](https://mybinder.org/v2/gh/gammapy/gammapy/v0.12?urlpath=lab/tree/source_population_model.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",
"[source_population_model.ipynb](../_static/notebooks/source_population_model.ipynb) |\n",
"[source_population_model.py](../_static/notebooks/source_population_model.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Astrophysical source population modeling with Gammapy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"The [gammapy.astro.population](..\/astro/population/index.rst) package contains some simple Galactic source population models.\n",
"\n",
"Here we provide some Python code to compute observable parameter distributions for Galactic gamma-ray source populations.\n",
"\n",
"* Observables: Flux, GLON, GLAT\n",
"* Source classes: Pulsar (PSR), Supernova remnant (SNR), pulsar wind nebula (PWN)\n",
"\n",
"References:\n",
"\n",
"* Section 6.2 in the Fermi-LAT collaboration paper [\"The First Fermi-LAT Catalog of Sources Above 10 GeV\"](http://adsabs.harvard.edu/abs/2013arXiv1306.6772T)\n",
"* Axel Donath's bachelor thesis [\"Modelling Galactic gamma-ray source populations\"](http://pubman.mpdl.mpg.de/pubman/item/escidoc:912132:1/component/escidoc:912131/BScThesis_ddonath.pdf), specifically Chapter 4.\n",
"* Casanova & Dingus (2008), [\"Constraints on the TeV source population and its contribution to the galactic diffuse TeV emission\"](http://adsabs.harvard.edu/abs/2008APh....29...63C)\n",
"* Strong (2007), [\"Source population synthesis and the Galactic diffuse gamma-ray emission\"](http://adsabs.harvard.edu/abs/2007Ap%26SS.309...35S)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import astropy.units as u\n",
"from gammapy.utils.random import sample_powerlaw\n",
"from gammapy.astro import population"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulate positions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Spatial distribution using Lorimer (2006) model\n",
"n_sources = int(1e5)\n",
"\n",
"table = population.make_base_catalog_galactic(\n",
" n_sources=n_sources,\n",
" rad_dis=\"L06\",\n",
" vel_dis=\"F06B\",\n",
" max_age=1e6 * u.yr,\n",
" spiralarms=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulate luminosities"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several source population models, e.g. the 1FHL paper or Strong (2007), use power-law luminosity functions.\n",
"\n",
"Here we implement the \"reference model\" from the 1FHL catalog paper section 6.2."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Source luminosity (ph s^-1)\n",
"\n",
"luminosity = sample_powerlaw(x_min=1e34, x_max=1e37, gamma=1.5, size=n_sources)\n",
"table[\"luminosity\"] = luminosity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute observable parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"
\n",
" name dtype unit description \n",
"---------- ------- --------- --------------------------------------\n",
" age float64 yr Age of the source\n",
" n_ISM float64 1 / cm3 Interstellar medium density\n",
" spiralarm str18 Which spiralarm?\n",
" x_birth float64 kpc Galactocentric x coordinate at birth\n",
" y_birth float64 kpc Galactocentric y coordinate at birth\n",
" z_birth float64 kpc Galactocentric z coordinate at birth\n",
" x float64 kpc Galactocentric x coordinate\n",
" y float64 kpc Galactocentric y coordinate\n",
" z float64 kpc Galactocentric z coordinate\n",
" vx float64 km / s Galactocentric velocity in x direction\n",
" vy float64 km / s Galactocentric velocity in y direction\n",
" vz float64 km / s Galactocentric velocity in z direction\n",
" v_abs float64 km / s Galactocentric velocity (absolute)\n",
"luminosity float64 \n",
" distance float64 pc Distance observer to source center\n",
" GLON float64 deg Galactic longitude\n",
" GLAT float64 deg Galactic latitude\n",
" VGLON float64 deg / Myr Velocity in Galactic longitude\n",
" VGLAT float64 deg / Myr Velocity in Galactic latitude\n",
" RA float64 deg Right ascension\n",
" DEC float64 deg Declination\n",
" flux float64 1 / kpc2 Source flux\n"
]
}
],
"source": [
"table = population.add_observed_parameters(table)\n",
"table.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simulation is done, you could save the simulated catalog to a file.\n",
"\n",
"Here we just plot a few distributions to check if the results look OK."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(table[\"x\"][:1000], table[\"y\"][:1000]);"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(table[\"GLON\"], bins=100);"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(table[\"GLAT\"], bins=100, log=True);"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(table[\"GLON\"][:1000], table[\"GLAT\"][:1000]);"
]
},
{
"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": [
"plt.hist(table[\"distance\"], bins=100, log=True);"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(np.log10(table[\"luminosity\"]), bins=100, log=True);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(np.log10(table[\"flux\"]), bins=100, log=True);"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# TODO: plot GLON, GLAT, FLUX distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"TODO"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Start exercises here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What next?\n",
"\n",
"TODO: summarise what was done here briefly.\n",
"\n",
"TODO: add some pointers to other documentation."
]
}
],
"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": "3.5.3"
},
"nbsphinx": {
"orphan": true
}
},
"nbformat": 4,
"nbformat_minor": 1
}