{ "cells": [ { "cell_type": "markdown", "id": "c189aa22", "metadata": {}, "source": [ "(kcorrect)=\n", "\n", "# K-correct Photometry" ] }, { "cell_type": "markdown", "id": "633c6c8d", "metadata": {}, "source": [ "This notebook shows how K-correction the photometry of a SLSN. First import the required packages." ] }, { "cell_type": "code", "execution_count": 1, "id": "e018bb2e", "metadata": {}, "outputs": [], "source": [ "from slsne.lcurve import get_kcorr, fit_map\n", "from slsne.utils import get_lc\n", "import numpy as np" ] }, { "cell_type": "markdown", "id": "170058fa", "metadata": {}, "source": [ "For this example, lets import a test light curve from the reference data directory. Note that you can obtain the `redshift` and `peak` from the `get_params` function, but we'll skip that step for this example." ] }, { "cell_type": "code", "execution_count": 2, "id": "da78a5a2", "metadata": {}, "outputs": [], "source": [ "# Get a light curve\n", "phot = get_lc('2013dg')\n", "# Define the redshift and date of peak\n", "redshift = 0.265\n", "peak = 56447.62" ] }, { "cell_type": "markdown", "id": "25cb769c", "metadata": {}, "source": [ "Now we can fit the light curve with a mean SLSN map, either with respect to peak (`peak`) or explosion (`boom`)." ] }, { "cell_type": "code", "execution_count": 3, "id": "5e382b24", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best fit is a map with Stretch: 1.14, Amplitude: 0.05, Offset: -8.00\n" ] } ], "source": [ "stretch, amplitude, offset = fit_map(phot, redshift, peak=peak)\n", "print(f\"Best fit is a map with Stretch: {stretch:.2f}, Amplitude: {amplitude:.2f}, Offset: {offset:.2f}\")" ] }, { "cell_type": "markdown", "id": "a13f8295", "metadata": {}, "source": [ "We can now use these best-fit values to get the optimal K-correction for the photometry." ] }, { "cell_type": "code", "execution_count": 4, "id": "0617340f", "metadata": {}, "outputs": [], "source": [ "K_corr = get_kcorr(phot, redshift, peak=peak, stretch=stretch, offset=offset)" ] }, { "cell_type": "markdown", "id": "4bec8f3c", "metadata": {}, "source": [ "And finally plot the data" ] }, { "cell_type": "code", "execution_count": 8, "id": "1687793a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from slsne.utils import calc_DM, plot_colors\n", "import matplotlib.pyplot as plt\n", "\n", "# Select band to plot\n", "band = 'i'\n", "use = (phot['Filter'] == band) & (phot['UL'] == 'False')\n", "\n", "# Calculate absolute magnitude\n", "DM = calc_DM(redshift)\n", "abs_mag = phot['Mag'][use] - DM\n", "\n", "# Calculate Phase\n", "phot['Phase'] = (phot['MJD'] - peak) / (1 + redshift)\n", "\n", "# Apply the simple and the optimal K-correction\n", "simple = abs_mag + 2.5 * np.log10(1 + redshift)\n", "optimal = abs_mag - K_corr[use]\n", "\n", "# Plot the light curves\n", "plt.errorbar(phot['Phase'][use], abs_mag, yerr = phot['MagErr'][use], fmt = 'o',\n", " color = plot_colors(band), alpha = 0.5, label = 'Observed Photometry')\n", "plt.errorbar(phot['Phase'][use], simple , yerr = phot['MagErr'][use], fmt = 'o',\n", " color = 'k', alpha = 0.5, label = r'$+ 2.5 \\log_{10}(1 + z)$')\n", "plt.errorbar(phot['Phase'][use], optimal, yerr = phot['MagErr'][use], fmt = 'o',\n", " color = 'b', alpha = 0.5, label = 'Corrected Photometry')\n", "\n", "plt.legend(loc = 'upper right')\n", "plt.ylim(-18, -22)\n", "plt.xlim(-20, 100)\n", "plt.xlabel('Phase [Days]')\n", "plt.ylabel(f'Absolute Mag [{band}]')\n", "plt.show();" ] } ], "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": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }