\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/v0.14?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\"](https://ui.adsabs.harvard.edu/abs/2013ApJS..209...34A)\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\"](https://ui.adsabs.harvard.edu/abs/2008APh....29...63C)\n",
"* Strong (2007), [\"Source population synthesis and the Galactic diffuse gamma-ray emission\"](https://ui.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": {
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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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STUnSaGaqvSNJGo2hL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhryv730HmH2LAS/AAAAAElFTkSuQmCC\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": "3.7.0"
},
"nbsphinx": {
"orphan": true
}
},
"nbformat": 4,
"nbformat_minor": 1
}