- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “c403face”, “metadata”: {}, “source”: [
“# Stellar Mass-Luminosity Biasn”, “n”, “Suppose that stars have a mass-luminosity relationship such that $Ln”, “\propto M^3$. If we have a flux-limited survey, it will bias usn”, “towards observing more massive stars which are not representative ofn”, “the full mass distribution. Let’s see how to set this up in popsynth. n”, “n”, “## Setup the problemn”, “First, we will assume that we have some initial mass function (IMF)n”, “for our stars that describes how there masses are distributed. Forn”, “simplicity, we will assume that this IMF is just a log-normal distribution:”]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “f767ee34”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:39.672167Z”, “iopub.status.busy”: “2022-02-09T16:33:39.669392Z”, “iopub.status.idle”: “2022-02-09T16:33:43.387363Z”, “shell.execute_reply”: “2022-02-09T16:33:43.387971Z”
}
}, “outputs”: [], “source”: [
“n”, “%matplotlib inlinen”, “n”, “import matplotlib.pyplot as pltn”, “from jupyterthemes import jtplotn”, “n”, “jtplot.style(context=”notebook”, fscale=1, grid=False)n”, “purple = “#B833FF”n”, “yellow = “#F6EF5B”n”, “n”, “import networkx as nxn”, “import numpy as npn”, “import warningsn”, “n”, “warnings.simplefilter(“ignore”)n”, “n”, “import popsynthn”, “n”, “# create a sampler for massn”, “# we do not directly observe the mass as it is a latent quantityn”, “n”, “initial_mass_function = popsynth.LogNormalAuxSampler(name=”mass”, observed = False)n”]
}, {
“cell_type”: “markdown”, “id”: “a33f9686”, “metadata”: {}, “source”: [
“We now assume the dependent variable is the luminosity, so we need an”, “DerivedLumAuxSampler that generates luminosities given a mass:”]
}, {
“cell_type”: “code”, “execution_count”: 2, “id”: “f49ed40e”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:43.394727Z”, “iopub.status.busy”: “2022-02-09T16:33:43.394184Z”, “iopub.status.idle”: “2022-02-09T16:33:43.398084Z”, “shell.execute_reply”: “2022-02-09T16:33:43.397591Z”
}
}, “outputs”: [], “source”: [
“class MassLuminosityRelation(popsynth.DerivedLumAuxSampler):n”, ” _auxiliary_sampler_name = “MassLuminosityRelation”n”, ” n”, ” def __init__(self, mu=0.0, tau=1.0, sigma=1):n”, ” # this time set observed=Truen”, ” super(MassLuminosityRelation, self).__init__(“mass_lum_relation”, uses_distance=False)n”, “n”, ” def true_sampler(self, size):n”, ” n”, “tt# the secondary quantity is mass n”, “ttn”, ” mass = self._secondary_samplers[“mass”].true_valuesn”, ” n”, “tt# we will store the log of mass cubedn”, ” self._true_values = 3 * np.log10(mass)n”, “n”, ” def compute_luminosity(self):n”, ” # compute the luminosityn”, “tt# from the relationn”, ” return np.power(10., self._true_values)n”, “n”, “n”, “luminosity = MassLuminosityRelation()n”, “n”]
}, {
“cell_type”: “markdown”, “id”: “6a0c695b”, “metadata”: {}, “source”: [
“Now we can put everything together. First, we need to assignn”, “mass as a secondary quantity to the luminosity”]
}, {
“cell_type”: “code”, “execution_count”: 3, “id”: “758e020f”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:43.402943Z”, “iopub.status.busy”: “2022-02-09T16:33:43.401719Z”, “iopub.status.idle”: “2022-02-09T16:33:43.403536Z”, “shell.execute_reply”: “2022-02-09T16:33:43.403951Z”
}
}, “outputs”: [], “source”: [
“luminosity.set_secondary_sampler(initial_mass_function)”]
}, {
“cell_type”: “markdown”, “id”: “bd07dc88”, “metadata”: {}, “source”: [
“Finally, we will use a simple spherical geometry to hold our stars. Wen”, “will also put a hard flux limit on our survey to simulate an”, “flux-limited catalog.”]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “af2cbe6a”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:43.408805Z”, “iopub.status.busy”: “2022-02-09T16:33:43.408279Z”, “iopub.status.idle”: “2022-02-09T16:33:43.410421Z”, “shell.execute_reply”: “2022-02-09T16:33:43.409994Z”
}
}, “outputs”: [], “source”: [
“pop_gen = popsynth.populations.SphericalPopulation(1, r_max=10)n”, “n”, “# create the flux selectionn”, “n”, “flux_selector = popsynth.HardFluxSelection()n”, “flux_selector.boundary = 1e-2n”, “pop_gen.set_flux_selection(flux_selector)n”, “n”, “# now add the luminisity samplern”, “n”, “pop_gen.add_observed_quantity(luminosity)n”]
}, {
“cell_type”: “markdown”, “id”: “c578cdf1”, “metadata”: {}, “source”: [
“Now let’s draw our survey.”]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “860d156f”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:43.414184Z”, “iopub.status.busy”: “2022-02-09T16:33:43.412434Z”, “iopub.status.idle”: “2022-02-09T16:33:43.641210Z”, “shell.execute_reply”: “2022-02-09T16:33:43.640493Z”
}
}, “outputs”: [
- {
- “data”: {
- “application/vnd.jupyter.widget-view+json”: {
- “model_id”: “dee392ea7b454ee4a67b4fc0de4aa217”, “version_major”: 2, “version_minor”: 0
}, “text/plain”: [
“Drawing distances: 0%| | 0/4125 [00:00<?, ?it/s]”]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“n”, “pop = pop_gen.draw_survey(flux_sigma=0.5)n”]
}, {
“cell_type”: “markdown”, “id”: “33f8732c”, “metadata”: {}, “source”: [
“We can now look at the distribution of the masses:n”]
}, {
“cell_type”: “code”, “execution_count”: 6, “id”: “4ba856af”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:43.650378Z”, “iopub.status.busy”: “2022-02-09T16:33:43.649757Z”, “iopub.status.idle”: “2022-02-09T16:33:43.838667Z”, “shell.execute_reply”: “2022-02-09T16:33:43.839245Z”
}, “tags”: [
“nbsphinx-thumbnail”]
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
- “<matplotlib.legend.Legend at 0x7fb0aad39eb0>”
]
}, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
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n”, “text/plain”: [
“<Figure size 576x504 with 1 Axes>”]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“fig, ax = plt.subplots()n”, “n”, “bins = np.linspace(0,20,50)n”, “n”, “ax.hist(pop.mass,n”, ” bins=bins,n”, ” label=’all’, n”, ” color=purple,n”, ” histtype=”step”,n”, ” lw=2n”, ” n”, ” )n”, “n”, “ax.hist(pop.mass[pop.selection],n”, ” bins=bins,n”, ” label=’selected’,n”, ” color=yellow,n”, ” histtype=”step”,n”, ” lw=2n”, ” )n”, “n”, “ax.set_xlabel(‘stellar mass’)n”, “ax.legend()n”]
}, {
“cell_type”: “markdown”, “id”: “12e97cd1”, “metadata”: {}, “source”: [
“We can see that indeed our selected masses are biased towards higher values. n”, “n”, “Let’s look in the mass-luminostiy plane:”]
}, {
“cell_type”: “code”, “execution_count”: 7, “id”: “6fc3d9fb”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:43.857147Z”, “iopub.status.busy”: “2022-02-09T16:33:43.841853Z”, “iopub.status.idle”: “2022-02-09T16:33:44.455939Z”, “shell.execute_reply”: “2022-02-09T16:33:44.455455Z”
}, “lines_to_next_cell”: 0
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
- “<matplotlib.legend.Legend at 0x7fb0aad39e80>”
]
}, “execution_count”: 7, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
“image/png”: 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“text/plain”: [
“<Figure size 576x504 with 1 Axes>”]
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