\n",
"**This is a fixed-text formatted version of a Jupyter notebook.**\n",
"\n",
"- Try online [](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.8?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": {},
"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": {},
"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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HIeBQkieBl4HrB1dhJ5IcBr5L/1fwj1XVDzusc1YdA36N/oeL3wc+2m05nfsscCHwwOC3n0er6saq8v00pKrOJnllv+dNwKFx93tu2M/RX033O0n+fjD2SeAg/dbyfvoz336jo/pWxW/GSlLjum7dSJKmzKCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalx/wd1cfDsfMPwsAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"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": {},
"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": {},
"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": {},
"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": {},
"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
}