\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/v0.10?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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5mP79hBcmec7girnrc/564LYkbwKeD/w4/Sv8eaqRqrow+O/FJB+h3/ac+bne1K2bJPuAdwK3VdX/DD11HDiY5HlJdgF7gM91UeMq5qnGifcPXkfHgbcNfn4bsNJvTOti0Ef+IHC2qv5k6Km5qTPJ1ssz0pL8KHAT/XsJnwHeOnhZpzVW1V1Vtb2qdtL/9/cPVfUrzFGNSa5O8mOXfwbeSH+yxezPdVVt2j/0b2A+CXxx8Of9Q8+9i34f8hxwc4c1/iL9K+bvAd8CHpy3Gge1vIn+zKWv0285zcP5vRd4Cvi/wd/hYfp924eArw3+++KOa/xZ+u2ELw/9O3zTPNUJvAr4wqDGx4HfH4y/nP7FxXngb4DndX3OB3XdCHx83moc1PKlwZ8zl/8/WY9z7TdjJalxm7p1I0mbgUEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Lj/h8XQhFX4liGkgAAAABJRU5ErkJggg==\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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\n",
"text/plain": [
""
]
},
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"needs_background": "light"
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"output_type": "display_data"
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"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."
]
}
],
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"display_name": "Python 3",
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"name": "python3"
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