{ "cells": [ { "cell_type": "markdown", "id": "1a342e58", "metadata": {}, "source": [ "# Astrophysics III: galaxy formation & evolution\n", "Moodle: https://go.epfl.ch/PHYS-465\n", "\n", "## Exercise 4\n", "Teaching assistant: Lucie Scharré
\n", "Email: lucie.scharre@epfl.ch" ] }, { "cell_type": "markdown", "id": "55040aa8", "metadata": {}, "source": [ "***" ] }, { "cell_type": "code", "execution_count": 1, "id": "3eaf8a3e", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mlp\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "8a092e75", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/luciescharre/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n", " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" ] } ], "source": [ "#%% #####################################################################\n", "### Luminosity distance from redshift ### \n", "\n", "# Exact numbers depend on values for H0, and the matter and vacuum density parameters! For our puposes these values are similar enough.\n", "# Option 1, requires Astropy, units can be adjusted with u.m, u.km, u.au,...\n", "\n", "from astropy.cosmology import WMAP9 as cosmo # https://docs.astropy.org/en/stable/install.html\n", "from astropy import units as u\n", "z=3\n", "d_L = cosmo.luminosity_distance(z).to(u.Mpc).value \n", "\n", "\n", "# Option 2\n", "import scipy.integrate as spi\n", "def luminosity_distance(z):\n", " c = 3e8 # Speed of light in m/s\n", " H0 = 69.6 *1e3 # in m/s/Mpc\n", " Omega_m = 0.286 # matter \n", " Omega_lambda = 0.714 #vacuum\n", "\n", " integrand = lambda z: 1.0 / (Omega_m * (1 + z)**3 + Omega_lambda)**0.5\n", " integral, _ = spi.quad(integrand, 0, z)\n", "\n", " d_L = (c / H0) * (1 + z) * integral\n", "\n", " return d_L\n", "\n", "\n", "# Luminosity from flux and z\n", "def L_calc(F, z):\n", " D = cosmo.luminosity_distance(z).to(u.cm)\n", " L = F*(4*np.pi*D**2)*u.erg*u.s**-1\n", " return L.value" ] }, { "cell_type": "code", "execution_count": 3, "id": "fa2d1fc5", "metadata": {}, "outputs": [], "source": [ "#%% #####################################################################\n", "### Useful literature relations ###\n", "\n", "# Kennicutt+1998 relation to get SFR from Halpha\n", "def SFR_Halpha(L_ha):\n", " SFR = 7.9e-42 * L_ha \n", " return SFR\n", "\n", "\n", "## Elbaz SFR-Mstar relation at z =0\n", "def Elbaz_SDSS(log10_Mstar, alpha=8.7):\n", " log10_SFR = np.log10(alpha) + 0.77*(log10_Mstar-10) \n", " return log10_SFR\n", "\n", "\n", "### BPT criteria ###\n", "\n", "# SF criterion, Kewley et al. 2001 \n", "def obscrit_OIII_NII_SF(log_n2_ha_ratio):\n", " log_o3_hb_ratio = 0.61/(log_n2_ha_ratio-0.47)+1.19\n", " return log_o3_hb_ratio\n", "\n", "\n", "# AGN criterion, Kauffmann et al. 2003\n", "def obscrit_OIII_NII_AGN(log_n2_ha_ratio):\n", " log_o3_hb_ratio = 0.61/(log_n2_ha_ratio-0.05)+1.3\n", " return log_o3_hb_ratio\n", "\n", "\n", "# Lamastra+09 relation to get LAGN from LOIII\n", "\n", "def LAGN_LOIII(L_o3):\n", " LAGN = np.zeros(len(SDSS_o3))\n", " \n", " #between 1e38 - 1e40\n", " COIII_1 = 87\n", "\n", " #between 1e40 - 1e42\n", " COIII_2 = 142\n", "\n", " #between 1e42 - 1e44\n", " COIII_3 = 454\n", " \n", " LAGN[L_o3<1e40] = COIII_1 * L_o3[L_o3<1e40]\n", " LAGN[(L_o3>1e40)&(L_o3<1e42)] = COIII_2 * L_o3[(L_o3>1e40)&(L_o3<1e42)]\n", " LAGN[L_o3>1e42] = COIII_3 * L_o3[L_o3>1e42]\n", " \n", " return LAGN\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "56d36029", "metadata": {}, "outputs": [], "source": [ "#%% #####################################################################\n", "### Snippet to plot contours ###\n", "\n", "from scipy.ndimage.filters import gaussian_filter\n", "\n", "# sigma (s) signifies standard deviation of the gaussian smoothing \n", "\n", "\n", "def contours(x, y, s=1, bins=100, nlevels = 7, alpha=0.8,cmap=\"Greys_r\"):\n", "\n", " hist, xedges, yedges = np.histogram2d(x, y, bins=bins)\n", " \n", " extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n", " heatmap = gaussian_filter(hist, sigma=s)\n", " \n", " # logarithmic contours\n", " levels = np.logspace(0,np.log10(heatmap.max()),nlevels)\n", "\n", " contourplot = plt.contour(heatmap.T,extent=extent, alpha=0.8,cmap=cmap,levels=levels)\n", "\n", "\n", "def plot_2dhist(x, y, bins=100, cmap = \"Greys_r\"):\n", " hist,xedges,yedges = np.histogram2d(x,y,bins=bins)\n", " extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n", " hist_plot = plt.imshow(hist.T, norm=mlp.colors.LogNorm(), cmap =cmap,extent = extent, interpolation=None,origin='lower',aspect='auto')\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "177f178e", "metadata": {}, "outputs": [], "source": [ "#%% #####################################################################\n", "### Snippet to read in SDSS data ###\n", "\n", "SDSS_data = np.loadtxt(\"/Users/luciescharre/Documents/Astro-III_Exercises/Exercises4_SDSS_EL (L - in process)/SDSS_DR7.txt\", comments='#').T\n", "\n", "\n", "SDSS_Mstar = 10 ** SDSS_data[-1]\n", "SDSS_z = SDSS_data[1]\n", "\n", "scaling = 1e-16\n", "\n", "SDSS_ha = SDSS_data[8]*scaling \n", "SDSS_hb = SDSS_data[2]*scaling\n", "SDSS_o3 = SDSS_data[4]*scaling\n", "SDSS_n2 = SDSS_data[12]*scaling\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c384b484", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }