\n",
"\n",
"**This is a fixed-text formatted version of a Jupyter notebook**\n",
"\n",
"- Try online [](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.11?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": {
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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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XRXpmrCQ1bm5aN5Kk3THoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklq3P8CnmXIaiBjIasAAAAASUVORK5CYII=\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
}