{
“cells”: [
{

“cell_type”: “markdown”, “id”: “a9a95cd0”, “metadata”: {

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}, “tags”: []

}, “source”: [

“# DMSpectra”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “bb0a55bb”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:14:42.983400Z”, “iopub.status.busy”: “2021-08-17T08:14:42.982232Z”, “iopub.status.idle”: “2021-08-17T08:14:45.941248Z”, “shell.execute_reply”: “2021-08-17T08:14:45.941972Z”

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“duration”: 2.982436, “end_time”: “2021-08-17T08:14:45.942421”, “exception”: false, “start_time”: “2021-08-17T08:14:42.959985”, “status”: “completed”

}, “tags”: []

}, “outputs”: [], “source”: [

“%%capturen”, “n”, “import numpy as npn”, “n”, “import matplotlib.pyplot as pltn”, “n”, “import warningsn”, “warnings.simplefilter("ignore")n”, “n”, “from astromodels.functions.function import _known_functionsn”, “n”, “n”, “from jupyterthemes import jtplotn”, “jtplot.style(context="talk", fscale=1, ticks=True, grid=False)n”, “%matplotlib inline”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “d7b27dba”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:14:45.973296Z”, “iopub.status.busy”: “2021-08-17T08:14:45.972027Z”, “iopub.status.idle”: “2021-08-17T08:14:45.974453Z”, “shell.execute_reply”: “2021-08-17T08:14:45.981180Z”

}, “nbsphinx”: “hidden”, “papermill”: {

“duration”: 0.031066, “end_time”: “2021-08-17T08:14:45.983525”, “exception”: false, “start_time”: “2021-08-17T08:14:45.952459”, “status”: “completed”

}, “tags”: [

“parameters”

]

}, “outputs”: [], “source”: [

“func_name = "TbAbs"n”, “n”, “x_scale="log"n”, “y_scale="log"n”, “n”, “linear_range = Falsen”, “n”, “wide_energy_range = False”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “83d1f305”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:14:46.012158Z”, “iopub.status.busy”: “2021-08-17T08:14:46.010034Z”, “iopub.status.idle”: “2021-08-17T08:14:46.013975Z”, “shell.execute_reply”: “2021-08-17T08:14:46.014599Z”

}, “papermill”: {

“duration”: 0.021601, “end_time”: “2021-08-17T08:14:46.014917”, “exception”: false, “start_time”: “2021-08-17T08:14:45.993316”, “status”: “completed”

}, “tags”: [

“injected-parameters”

]

}, “outputs”: [], “source”: [

“# Parametersn”, “func_name = "DMSpectra"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “580becac”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:14:46.099408Z”, “iopub.status.busy”: “2021-08-17T08:14:46.088017Z”, “iopub.status.idle”: “2021-08-17T08:14:46.114273Z”, “shell.execute_reply”: “2021-08-17T08:14:46.112935Z”

}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {

“duration”: 0.09013, “end_time”: “2021-08-17T08:14:46.114568”, “exception”: false, “start_time”: “2021-08-17T08:14:46.024438”, “status”: “completed”

}, “tags”: []

}, “outputs”: [], “source”: [

“func = _known_functions[func_name]()n”, “n”, “if wide_energy_range:n”, “n”, ” energy_grid = np.geomspace(1e2,1e4,500)n”, ” n”, “else:n”, ” n”, ” energy_grid = np.geomspace(2e-1,1e1,1000)n”, “n”, “if linear_range:n”, “n”, “tenergy_grid = np.linspace(-5,5,1000)n”, “n”, ” n”, “blue = "#4152E3"n”, “red = "#E3414B"n”, “green = "#41E39E"”

]

}, {

“cell_type”: “markdown”, “id”: “47b3234d”, “metadata”: {

“lines_to_next_cell”: 0, “papermill”: {

“duration”: 0.009914, “end_time”: “2021-08-17T08:14:46.134249”, “exception”: false, “start_time”: “2021-08-17T08:14:46.124335”, “status”: “completed”

}, “tags”: []

}, “source”: [

“## Description”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “9aeaf28a”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:14:46.196143Z”, “iopub.status.busy”: “2021-08-17T08:14:46.194993Z”, “iopub.status.idle”: “2021-08-17T08:14:46.208374Z”, “shell.execute_reply”: “2021-08-17T08:14:46.212688Z”

}, “papermill”: {

“duration”: 0.069318, “end_time”: “2021-08-17T08:14:46.213073”, “exception”: false, “start_time”: “2021-08-17T08:14:46.143755”, “status”: “completed”

}, “tags”: []

}, “outputs”: [

{
“data”: {
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“<ul>n”, “n”, “<li>description: Class that evaluates the spectrum for a DM particle of a given mass, channel, cross section, and J-factor. Combines Pythia-based tables from both Fermi (2 GeV < m_DM < 10 TeV) and HAWC (10 TeV < m_dm < 1 PeV)n”, “The parameterization is given byn”, “F(x) = 1 / (8 * pi) * (1/mass^2) * sigmav * J * dN/dE(E,mass,i)n”, “Note that this class assumes that mass and J-factor are provided in units of GeV and GeV^2 cm^-5</li>n”, “n”, “<li>formula: $$</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>mass: n”, “<ul>n”, “n”, “<li>value: 10.0</li>n”, “n”, “<li>desc: DM mass (GeV)</li>n”, “n”, “<li>min_value: None</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 1.0</li>n”, “n”, “<li>free: False</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>channel: n”, “<ul>n”, “n”, “<li>value: 4.0</li>n”, “n”, “<li>desc: DM annihilation channel</li>n”, “n”, “<li>min_value: None</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.4</li>n”, “n”, “<li>free: False</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>sigmav: n”, “<ul>n”, “n”, “<li>value: 1e-26</li>n”, “n”, “<li>desc: DM annihilation cross section (cm^3/s)</li>n”, “n”, “<li>min_value: None</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 1e-27</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>J: n”, “<ul>n”, “n”, “<li>value: 1e+20</li>n”, “n”, “<li>desc: Target total J-factor (GeV^2 cm^-5)</li>n”, “n”, “<li>min_value: None</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 1e+19</li>n”, “n”, “<li>free: False</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”

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” * description: ‘Class that evaluates the spectrum for a DM particle of a given mass,n”, ” * channel, cross section, and J-factor. Combines Pythia-based tables from both Fermin”, ” * (2 GeV < m_DM < 10 TeV) and HAWC (10 TeV < m_dm < 1 PeV)n”, “n”, ” * The parameterization is given byn”, “n”, ” * F(x) = 1 / (8 * pi) * (1/mass^2) * sigmav * J * dN/dE(E,mass,i)n”, “n”, ” * Note that this class assumes that mass and J-factor are provided in units of GeVn”, ” * and GeV^2 cm^-5’n”, ” * formula: $$n”, ” * parameters:n”, ” * mass:n”, ” * value: 10.0n”, ” * desc: DM mass (GeV)n”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 1.0n”, ” * free: falsen”, ” * channel:n”, ” * value: 4.0n”, ” * desc: DM annihilation channeln”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.4n”, ” * free: falsen”, ” * sigmav:n”, ” * value: 1.0e-26n”, ” * desc: DM annihilation cross section (cm^3/s)n”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 1.0e-27n”, ” * free: truen”, ” * J:n”, ” * value: 1.0e+20n”, ” * desc: Target total J-factor (GeV^2 cm^-5)n”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 1.0e+19n”, ” * free: false”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“func.display()”

]

}, {

“cell_type”: “markdown”, “id”: “59d59ff9”, “metadata”: {

“papermill”: {

“duration”: 0.010807, “end_time”: “2021-08-17T08:14:46.234394”, “exception”: false, “start_time”: “2021-08-17T08:14:46.223587”, “status”: “completed”

}, “tags”: []

}, “source”: [

“## Shape n”, “n”, “The shape of the function. n”, “n”, “If this is not a photon model but a prior or linear function then ignore the units as these docs are auto-generated

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “eeeca879”, “metadata”: {

“execution”: {

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“nbsphinx-thumbnail”

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n”, “text/plain”: [

“<Figure size 432x288 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“fig, ax = plt.subplots()n”, “n”, “n”, “ax.plot(energy_grid, func(energy_grid), color=blue)n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel("photon flux")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”

]

}, {

“cell_type”: “markdown”, “id”: “df091984”, “metadata”: {

“lines_to_next_cell”: 0, “papermill”: {

“duration”: 0.014013, “end_time”: “2021-08-17T08:14:47.339904”, “exception”: false, “start_time”: “2021-08-17T08:14:47.325891”, “status”: “completed”

}, “tags”: []

}, “source”: [

“## F$_{\nu}$n”, “n”, “The F$_{\nu}$ shape of the photon modeln”, “if this is not a photon model, please ignore this auto-generated plot

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “f4ecb4b6”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:14:47.423634Z”, “iopub.status.busy”: “2021-08-17T08:14:47.395420Z”, “iopub.status.idle”: “2021-08-17T08:14:48.364072Z”, “shell.execute_reply”: “2021-08-17T08:14:48.365224Z”

}, “papermill”: {

“duration”: 1.01368, “end_time”: “2021-08-17T08:14:48.365534”, “exception”: false, “start_time”: “2021-08-17T08:14:47.351854”, “status”: “completed”

}, “tags”: []

}, “outputs”: [

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 432x288 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“fig, ax = plt.subplots()n”, “n”, “ax.plot(energy_grid, energy_grid * func(energy_grid), red)n”, “n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel(r"energy flux (F$_{\nu}$)")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”, “n”

]

}, {

“cell_type”: “markdown”, “id”: “c613febd”, “metadata”: {

“papermill”: {

“duration”: 0.012685, “end_time”: “2021-08-17T08:14:48.390932”, “exception”: false, “start_time”: “2021-08-17T08:14:48.378247”, “status”: “completed”

}, “tags”: []

}, “source”: [

“## $\nu$F$_{\nu}$n”, “n”, “The $\nu$F$_{\nu}$ shape of the photon modeln”, “if this is not a photon model, please ignore this auto-generated plot

]

}, {

“cell_type”: “code”, “execution_count”: 8, “id”: “f279d548”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:14:48.463864Z”, “iopub.status.busy”: “2021-08-17T08:14:48.439352Z”, “iopub.status.idle”: “2021-08-17T08:14:49.440106Z”, “shell.execute_reply”: “2021-08-17T08:14:49.440904Z”

}, “papermill”: {

“duration”: 1.036865, “end_time”: “2021-08-17T08:14:49.441219”, “exception”: false, “start_time”: “2021-08-17T08:14:48.404354”, “status”: “completed”

}, “tags”: []

}, “outputs”: [

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 432x288 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“fig, ax = plt.subplots()n”, “n”, “ax.plot(energy_grid, energy_grid**2 * func(energy_grid), color=green)n”, “n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel(r"$\nu$F$_{\nu}$")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”

]

}

], “metadata”: {

“jupytext”: {

“formats”: “ipynb,md”

}, “kernelspec”: {

“display_name”: “Python 3”, “language”: “python”, “name”: “python3”

}, “language_info”: {

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