{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Priors\n", "\n", "The priors used in our code have been written to exemplify what we believe to be probable values for every parameter.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# load in necessary packages\n", "import numpy as np\n", "import scipy.stats\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mean\n", "\n", "Most normalized or detrended magnitudes tend to hover around 0. Our fluxes were normalized around a value of 1, so we make the mean for our normal distribution 1 with a small sigma of 0.5 so that if other data uses 0 as it's mean, it shouldn't have any significant effect on the likelihood." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mean = np.arange(-4, 5, 0.1)\n", "\n", "p_mean = scipy.stats.norm(1, 0.5).logpdf(mean)\n", "\n", "plt.plot(mean, np.exp(p_mean))\n", "plt.vlines(1, 0, 0.8, alpha=0.5, label=\"Mean=1, sigm=0.5\")\n", "plt.title(\"Mean Prior\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Amplitude (long kernel)\n", "\n", "Our kernel modeling long-term changes in the profile of the lightcurve has a hyper-parameter for the amplitude. While we don't expect large changes in the amplitudes over time, we also didn't want to exclude any values (except for negative values) so we chose a prior with a peak at 2, and a large sigma.\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "log_amp_l = np.arange(-5, 5, 0.1)\n", "\n", "p_log_amp_k2 = scipy.stats.norm(np.log(2), np.log(10)).logpdf(log_amp_l)\n", "\n", "plt.plot(np.exp(log_amp_l), np.exp(p_log_amp_k2))\n", "plt.vlines(2, 0, 0.2, alpha=0.5, label=\"Mean=2, sigma=10\")\n", "plt.title(\"Amplitude Prior\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Metric (long kernel)\n", "\n", "The metric hyper-parameter for the long-term kernel is expected to capture any gradual changes to the lightcurve profile over time, meaning that we want the average time to be quite long, as to discourage it from trying to fit for any short term changes, since those should be coming from the periodicity of the asteroid. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "log_metric = np.arange(-5, 10, 0.1)\n", "\n", "p_log_metric = scipy.stats.norm(np.log(100), np.log(10)).logpdf(log_metric)\n", "\n", "plt.plot(np.exp(log_metric), np.exp(p_log_metric))\n", "plt.vlines(100, 0, 0.2, alpha=0.5, label=\"Mean=100,, sigma=10\")\n", "plt.title(\"Metric Prior\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Amplitude (periodic)\n", "\n", "The amplitude of the periodic kernel is expected to be similar to the difference in magnitude of the asteroid, while the other amplitude is meant to model more of the change in the mean of the amplitude over extended periods of time. The periodic amplitude is thus expected to vary by a few magnitudes potentially, but never anything extensive.\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "log_amp_p = np.arange(-3, 3, 0.01)\n", "\n", "p_log_amp_k1 = scipy.stats.norm(np.log(2), np.log(2)).logpdf(log_amp_p)\n", "\n", "plt.plot(np.exp(log_amp_p), np.exp(p_log_amp_k1))\n", "plt.vlines(2, 0, 0.8, alpha=0.5, label=\"Mean=2, sigma=2\")\n", "plt.title(\"Amplitude (Periodic) Prior\")\n", "#plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gamma\n", "\n", "Gamma determines the length-scale of the variability of the asteroid profile. The smaller the value, the smoother the lightcurve is expected to look, versus a higher value for gamma indicates a lot of detail within the correlating period. \n", "\n", "If gamma becomes unusually large, it might be because the estimated period is capturing multiple period cycles, and is thus interpreting the lightcurve to be more complex than it actually is." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/christina/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gamma = np.arange(0.0001 ,50, 0.01)\n", "\n", "p_log_gamma = scipy.stats.norm(np.log(10), np.log(2)).logpdf(np.log(gamma))\n", "\n", "plt.plot(gamma, np.exp(p_log_gamma))\n", "plt.vlines(10, 0, 0.8, alpha=0.5, label=\"Mean=10, sigma=2\")\n", "plt.title(\"Gamma Prior\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Period\n", "\n", "The period is the most anticipated parameter we are looking to fit. We know from previous detailed studies of asteroids what we would expect the general distribution of asteroid periods to look like, so we are replicating the general distribution here. Most asteroids you would expect to have a period within 24-48 hours, with little chance of a period being faster than 2 hours (although not impossible). " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "log_period = np.arange(-3, 1, 0.01)\n", "\n", "p_log_period = scipy.stats.norm(np.log(4.0 / 24.0), (12.0 / 24.0)).logpdf(log_period)\n", "\n", "plt.plot(np.exp(log_period)*24, np.exp(p_log_period))\n", "plt.vlines(4, 0, 0.8, alpha=0.5, label=\"Mean=4, sigma=12\")\n", "plt.title(\"Period Prior\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 2 }