Scipy2014_Talk.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from yt.funcs import mylog\n",
      "import logging\n",
      "mylog.setLevel(logging.ERROR)\n",
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
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    {
     "cell_type": "markdown",
     "metadata": {
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       "slide_type": "slide"
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     },
     "source": [
      "# Simulating X-ray Observations with Python\n",
      "\n",
      "### John ZuHone (NASA/GSFC, U. Maryland)\n",
      "\n",
      "with \n",
      "\n",
      "Veronica Biffi (SISSA), Eric Hallman (U. Colorado),    \n",
      "Scott Randall (Harvard-CfA), Adam Foster (Harvard-CfA),     \n",
      "Christian Schmid (Dr. Karl Remeis-Sternwarte & ECAP)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## X-ray Astronomy Probes the High-Energy Universe"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* High-temperature plasmas (galaxy clusters, solar corona)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Relativistic cosmic rays (inverse-Compton scattering of the CMB, active galactic nuclei)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<img src=\"telescopes.png\" align=\"center\">\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<img src=\"xrays.png\" align=\"center\">\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Small-Number Statistics!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"counts.png\" align=\"center\">\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Image credit Dan Wik, NASA/GSFC"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Simulations vs. Observations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Simulations are 3D (well, the best ones are); observations are 2D projections"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Simulations consist of physical quantities like density, temperature, velocity, composition; observations are collections of photons which are emitted from physical processes that depend on those quantities"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Simulations generally only model what you put into them (we hope); observations include contaminating backgrounds and instrumental effects"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Bridging the Gap with PHOX"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Biffi et al 2012, ApJ; http://www.mpa-garching.mpg.de/~kdolag/Phox/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Most previous attempts at generating synthetic X-ray observations generated flux images projected along a line of sight, then used this image as a map to generate photon samples"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* This works well, but if you want to simulate observations along many different lines of sight for many different instruments, it's expensive"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* ``PHOX`` takes a different approach; generate a large sample of photons in 3D first (the most expensive step) ONCE (essentially discretize the photon emissivity function)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Then every time you want to make an observation, you simply draw a subset of them and project along a line of sight"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<img src=\"pipeline.png\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Why use yt?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* http://yt-project.org"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* If you really want to know, you should come to Nathan Goldbaum's talk at 3:50 in this room!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Reads simulation data from a variety of simulation codes (FLASH, Enzo, Gadget, etc.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Provides windows onto the data that make more sense from a *physical* perspective, not only a *computational* perspective"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Instead of thinking about cells, particles, and I/O, think about things like spheres, disks, fields, etc."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Make use of the rest of the scientific Python ecosystem for astronomy (SciPy, AstroPy, APLpy, etc.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Creating a Synthetic Observation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* This example code will be in the proceedings"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* As well as a notebook at http://www.jzuhone.com/files/photon_simulator_example.ipynb"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* This code will be implemented in ``yt`` v. 3.0 beta (though it is also part of the current stable version, 2.6.2)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* ``yt.analysis_modules.photon_simulator``"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import yt\n",
      "from yt.analysis_modules.photon_simulator.api \\\n",
      "    import TableApecModel, ThermalPhotonModel, \\\n",
      "    PhotonList, TableAbsorbModel\n",
      "from yt.utilities.cosmology import Cosmology"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 2
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parameters={\"time_unit\":(1.0,\"Myr\"),\n",
      "            \"length_unit\":(1.0,\"Mpc\"),\n",
      "            \"mass_unit\":(1.0e14,\"Msun\")}\n",
      "\n",
      "ds = yt.load(\"MHDSloshing/virgo_low_res.0054.vtk\",\n",
      "             parameters=parameters)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sp = ds.sphere(\"c\", (250., \"kpc\"))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<img src=\"sloshing.png\" align=\"center\">\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Step 1: Generating the Original Photon Sample"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Models for Generating Photons"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Imagine we have some extended object with density, temperature, velocity, metallicity, etc. as a function of position"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* Spectral models define a X-ray emissivity in ${\\rm photons~s^{-1}~cm^{-3}~keV^{-1}}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<img src=\"schematic.png\" align=\"center\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Emissivity:\n",
      "\n",
      "$\\epsilon_E^\\gamma = n_en_H\\Lambda_E(T,Z)~{\\rm photons~s^{-1}~cm^{-3}~keV^{-1}}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"model_spec.png\">"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "atomdb_path = \"/Users/jzuhone/Data/atomdb\"\n",
      "\n",
      "apec_model = TableApecModel(atomdb_path,\n",
      "                            0.01, 10.0, 2000,\n",
      "                            apec_vers=\"2.0.2\",\n",
      "                            thermal_broad=False)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "thermal_model = ThermalPhotonModel(apec_model, \n",
      "                                   X_H=0.75, \n",
      "                                   Zmet=0.3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## How many photons?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* We want a *large* number of photons compared to the amount we might expect to get in an observation, so that the sample is statistically representative."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* The number of photons received by the observer is basically determined by the distance to the source $D_A$, the collecting area of the instrument $A_{\\rm det}$, and the exposure time of the instrument $t_{\\rm exp}$."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* So choose values for these parameters that are much larger than realistic values"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Flux: \n",
      "\n",
      "$F^{\\gamma}_i = \\frac{\\Delta{V}_i\\int{\\epsilon_E^\\gamma}dE}{4\\pi{D_{A,0}^2}(1+z_0)^2}~{\\rm photons~s^{-1}~cm^{-2}}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Number of Photons:\n",
      "    \n",
      "$N_{\\rm phot} = t_{\\rm exp,0}A_{\\rm det,0}\\displaystyle\\sum_i{F^{\\gamma}_i}~{\\rm photons}$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = 6000. # must be in cm**2!\n",
      "exp_time = 4.0e5 # must be in seconds!\n",
      "redshift = 0.05\n",
      "cosmo = Cosmology() # WMAP7 cosmology by default, \n",
      "                    # can pass in other values for parameters"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "photons = PhotonList.from_scratch(sp, redshift, A, \n",
      "                                  exp_time,\n",
      "                                  thermal_model, \n",
      "                                  center=\"c\",\n",
      "                                  cosmology=cosmo)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "photons.write_h5_file(\"my_photons.h5\")\n",
      "#photons = PhotonList.from_file(\"my_photons.h5\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
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     },
     "source": [
      "# Step 2: Projecting Photons to Create Observations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<img src=\"schematic.png\" align=\"center\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "We need to choose a line-of-sight vector $\\hat{n}$ along which the 3D photons will be projected to produce an image. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "L = [0.0,0.0,1.0] # line-of-sight vector"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "The photons will be Doppler-shifted according to the velocity of the local gas element. They will also be redshifted by the cosmological expansion.\n",
      "\n",
      "$E_1 = E_0\\sqrt{\\frac{c+v_{z'}}{c-v_{z'}}}\\frac{1}{1+z}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Some photons get absorbed by foreground galactic gas. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N_H = 0.1 # 10^22 cm**-2\n",
      "a_mod = TableAbsorbModel(\"tbabs_table.h5\", N_H) "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "How many photons get observed?\n",
      "\n",
      "$f = \\frac{t_{\\rm exp}}{t_{\\rm exp,0}}\\frac{A_{\\rm det}}{A_{\\rm det,0}}\\frac{D_{A,0}^2}{D_A^2}\\frac{(1+z_0)^3}{(1+z)^3}$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "texp = 1.0e5 # realistic exposure\n",
      "z = 0.07 # realistic redshift"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events = photons.project_photons(L, \n",
      "                                 exp_time_new=texp, \n",
      "                                 redshift_new=z, \n",
      "                                 absorb_model=a_mod)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Step 3: Modeling Instrumental Effects"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "* Real astronomical instruments have a number of effects that alter the distribution of photons that is actually \"observed\"."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* The \"effective area\" of the instrument is typically energy-dependent. Encapsulated in an \"ancillary response file\" (ARF)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* The photon energies are mapped to CCD channels in a non-trivial way. Encapsulated in an \"redistribution matrix file\" (RMF)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* The ``photon_simulator`` analysis module provides a simple way to convolve photons with these responses. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ARF = \"chandra_ACIS-S3_onaxis_arf.fits\"\n",
      "RMF = \"chandra_ACIS-S3_onaxis_rmf.fits\"\n",
      "resp = [ARF,RMF]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events = photons.project_photons(L, \n",
      "                                 exp_time_new=texp, \n",
      "                                 redshift_new=z, \n",
      "                                 absorb_model=a_mod,\n",
      "                                 responses=resp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "* However, sometimes a fuller simulation of a particular instrument is needed. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "* For this, we provide output formats for export to existing software packages for simulating X-ray instruments."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "For the ``MARX`` *Chandra* simulator, an HDF5 format in conjunction with a user source model:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events.write_h5_file(\"my_events.h5\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "For ``SIMX`` and ``Sixte``, two packages that simulate a variety of instruments, output in the ``SIMPUT`` format:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events = photons.project_photons(L, \n",
      "                                 exp_time_new=texp, \n",
      "                                 redshift_new=z, \n",
      "                                 absorb_model=a_mod)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events.write_simput_file(\"my_events\", \n",
      "                         clobber=True, \n",
      "                         emin=0.1, emax=10.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Image"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "<img src=\"aplpy_figure.png\" align=\"center\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Spectrum"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"spectrum.png\" align=\"center\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Simulate Different Instruments"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "<img src=\"comparison.png\" align=\"center\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "* You don't have to use data from a simulation\n",
      "* In-memory datasets based on NumPy arrays work too!\n",
      "* Spherically symmetric cluster with bubbles evacuated"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"bubbles.png\" align=\"center\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Summary\n",
      "\n",
      "* Bridging the gap between simulations and observations is essential in astrophysics\n",
      "* Implemented a synthetic X-ray observation generator (based on ``PHOX``) in ``yt``\n",
      "* Great for use for comparison with real observations, or as a tool for proposals\n",
      "* ``yt.analysis_modules.photon_simulator``\n",
      "* http://yt-project.org/docs/dev-3.0/analyzing/analysis_modules/photon_simulator.html"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}