diff --git a/README.rst b/README.rst deleted file mode 100644 index a1f1dd2..0000000 --- a/README.rst +++ /dev/null @@ -1 +0,0 @@ -Browse the branches to see the different notebooks I've been working on! \ No newline at end of file diff --git a/ccd_QE.csv b/ccd_QE.csv new file mode 100644 index 0000000..287d209 --- /dev/null +++ b/ccd_QE.csv @@ -0,0 +1,69 @@ +100, 0.0, 0, 0 +309.4560021465238, 70.30747424892068, 16, 40 +319.539015188068, 73.11442326755378, 14, 30.35714285714286 +329.9917387078021, 75.26541144025542, 15, 35.46666666666667 +339.70504127115635, 76.58538173145493, 14, 30.35714285714286 +349.7880543127005, 74.70200948669466, 10, 16.1 +379.53294278525584, 77.10351031374742, 12, 23.66666666666667 +390.00376402070555, 81.79762370567437, 13, 26.153846153846164 +399.57293120532483, 84.52897199856781, 16, 41.1875 +409.3114413012829, 87.18136275806562, 15, 35.46666666666667 +419.59611460365807, 89.04058920481614, 15, 36.26666666666667 +429.14136694965316, 91.16038905508852, 10, 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25.094467475669248, 17, 45.882352941176464 +969.6772918024916, 21.397142155426735, 14, 30.357142857142854 +979.7603048440358, 18.43928189923271, 14, 30.357142857142854 +990.0593824507558, 14.892867816551103, 14, 30.35714285714286 +1100.0, 0.0, 0, 0 diff --git a/observing_examples.ipynb b/observing_examples.ipynb new file mode 100644 index 0000000..1d945d4 --- /dev/null +++ b/observing_examples.ipynb @@ -0,0 +1,1137 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# synphot: Predicting count rates with ground-based and space-based telescopes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Authors\n", + "Tiffany Jansen, Brett Morris, Pey Lian Lim, & Erik Tollerud" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Objectives\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Keywords\n", + "synphot, synthetic photometry, astropy, astroquery, astronomy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "In this tutorial we will:\n", + "
    \n", + "
  1. Query the properties of our target star (HAT-P-11 to start)
    2. \n", + "
  2. Download model spectra from PHOENIX
    3. \n", + "
  3. Construct a source spectrum
    4. \n", + "
  4. Create the bandpass of observation
    5. \n", + "
  5. Model attenuation by the atmosphere
    6. \n", + "
  6. Model the effect of the quantum efficiency
    7. \n", + "
  7. Combine all of the effects and \"observe\"
    8. \n", + "
  8. Compute the count rate and total counts
    9. \n", + "
  9. Estimate the expected signal to noise ratio
    10. \n", + "
  10. Look at another example, this time with TRAPPIST-1
    11. \n", + "
  11. Simulate observations with a space-based telescope
    12. \n", + "
  12. Compare simulated counts to observed counts
    13. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import astropy.units as u\n", + "from synphot import units" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1. Query the properties of HAT-P-11" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we get the star's parameters using astroquery's Gaia query and astropy's SkyCoord: " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query finished.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: W35: None:5:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:6:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:7:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:8:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:10:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W27: None:11:0: W27: COOSYS deprecated in VOTable 1.2 [astropy.io.votable.tree]\n", + "WARNING: W06: None:48:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:51:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:54:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:57:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:60:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:123:0: W50: Invalid unit string 'mas**-2' [astropy.io.votable.tree]\n", + "WARNING: W50: None:126:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n", + "WARNING: W06: None:129:0: W06: Invalid UCD 'em.wavenumber;stat.error': Primary word 'stat.error' is not valid as a secondary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:129:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n", + "WARNING: W06: None:141:0: W06: Invalid UCD 'pos.errorEllipse;stat.max': Secondary word 'pos.errorEllipse' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:156:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:159:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n", + "WARNING: W06: None:162:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:171:0: W50: Invalid unit string ''electron'.s**-1' (suppressing further warnings of this type...) [astropy.io.votable.tree]\n", + "WARNING: W06: None:177:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W06: None:192:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W06: None:204:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:207:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" + ] + } + ], + "source": [ + "from astropy.coordinates import SkyCoord\n", + "from astroquery.gaia import Gaia\n", + "\n", + "ID = 'HAT-P-11'\n", + "\n", + "coord = SkyCoord.from_name(ID)\n", + "width = u.Quantity(1, u.arcmin) # width / height of search\n", + "height = u.Quantity(1, u.arcmin)\n", + "search_results = Gaia.query_object_async(coordinate=coord, width=width, height=height)\n", + "\n", + "# the queried star will be the one nearest to the given coordinates\n", + "search_results.add_index('dist', unique=True)\n", + "hatp11_info = search_results.loc['dist', min(search_results['dist'])]\n", + "\n", + "# now get the parameters\n", + "stellar_radius = hatp11_info['radius_val'] * u.R_sun\n", + "# divide 1 AU by parallax (arcseconds) to get distance in parsecs.\n", + "# parallax is given in milliarcseconds, so multiply by 1000: \n", + "distance = (1 / (hatp11_info['parallax']) * 1000) * u.pc\n", + "T_eff = hatp11_info['teff_val']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2. Download a model spectrum for HAT-P-11 from PHOENIX:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from astropy.io import fits\n", + "\n", + "T_eff = round(T_eff, -2) # round to nearest 100 K\n", + "\n", + "url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", + " 'PHOENIX-ACES-AGSS-COND-2011/Z-0.0/lte{T_eff:05d}-{log_g:1.2f}-0.0.PHOENIX-'\n", + " 'ACES-AGSS-COND-2011-HiRes.fits').format(T_eff=int(T_eff), log_g=4.5)\n", + "# Unit from PHOENIX model file:\n", + "flux = fits.getdata(url) * (u.erg / u.s / u.cm ** 2 / u.cm)\n", + "\n", + "wavelength_url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", + " 'WAVE_PHOENIX-ACES-AGSS-COND-2011.fits')\n", + "wavelengths = fits.getdata(wavelength_url) * u.Angstrom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 3. Construct a `synphot` source spectrum object from the above arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from synphot.spectrum import SourceSpectrum\n", + "from synphot.models import Empirical1D\n", + "\n", + "photlam_hatp11 = SourceSpectrum(Empirical1D,\n", + " points=wavelengths, lookup_table=flux)\n", + "\n", + "# Scale the flux to get the value at Earth\n", + "photlam_hatp11 = photlam_hatp11 * float(stellar_radius / distance) ** 2 / np.pi\n", + "\n", + "photlam_hatp11.plot(flux_unit='Jy', left=0, right=60000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(for other methods of constructing source spectra with `synphot`, see the first bulleted list here) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Now we will simulate the observation with a specific instrument" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Let's observe HAT-P-11 with the ARCTIC instrument on APO's 3.5m telescope." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "aperture_radius = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", + "aperture_area = np.pi * aperture_radius ** 2\n", + "gain = 1.9 # the gain of this detector in e-/ADU" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Create the bandpass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The bandpasses we will use are the SDSS r- and z- filters, whose transmission functions we query from SVO using the tynt package. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To construct a bandpass from an array with `synphot`, use SpectralElement with the Empirical1D model:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: W42: /var/folders/bn/d7v8bk_16cjg8qcm1gj03m6h0000gp/T/tmp46vg3hd6:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n", + "WARNING: W42: /var/folders/bn/d7v8bk_16cjg8qcm1gj03m6h0000gp/T/tmphlz0oi7e:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import tynt\n", + "from synphot.spectrum import SpectralElement\n", + "\n", + "sdss = ['rprime', 'zprime'] # only want r' and z' bands for now\n", + "\n", + "f = tynt.FilterGenerator()\n", + "\n", + "bandpasses = {}\n", + "for band in sdss:\n", + " filt = f.download_true_transmittance('SLOAN/SDSS.' + band + '_filter')\n", + " bp = SpectralElement(Empirical1D,\n", + " points=filt.wavelength,\n", + " lookup_table=filt.transmittance)\n", + " bandpasses[band] = bp\n", + " \n", + " plt.plot(bp.waveset, bp(bp.waveset), label=band)\n", + "plt.legend(loc='lower right')\n", + "plt.xlim(3000, 14000)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also construct a bandpass from a file using synphot's SpectralElement.from_file()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 5. Model the attenuation by the atmosphere" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Model the atmospheric transmittance using the local script `skymodel.py`, which queries results from the SkyCalc Sky Model Calculator:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import skymodel\n", + "\n", + "trans_waves, transmission = skymodel.get_atmospheric_transmittance(airmass=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `synphot` to convolve the atmospheric effects with other parts of the observation:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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F5t1w6bg+XofgGS2VKS8kWn30ANYKabUAxpj5WBPcZa33V25lcNdSykqSq/8N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\nvtbKYTn16dm2z0oKnTQpqCx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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "atmosphere = SpectralElement(Empirical1D,\n", + " points=trans_waves,\n", + " lookup_table=transmission)\n", + "atmosphere.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 6. Model the effect of the CCD's quantum efficiency on the spectrum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this we use the values in the table found in section 3.5 on this page." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quantum_efficiency = SpectralElement.from_file('ccd_QE.csv', wave_unit=\"nm\")\n", + "quantum_efficiency = quantum_efficiency / 100 # convert percentages to decimals\n", + "\n", + "quantum_efficiency.plot(left=3000, right=14000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 7. Combine all of the effects and \"observe\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have all of the effects on the source spectrum set, we can combine them together by simply multiplying the SpectralElement objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ubH9HPX7h5ylgafi38DCwrOxv5aC/kXrb51C+iOSxG78C5h7JY5z5H37GB2Qh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\niEjVlERERKRqSiIiIlI1JREREanafwN4rK991yPxRgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bp_atmos_qe = {} # setting up a dictionary for the different SDSS bands\n", + "for band in sdss:\n", + " bp_atmos_qe[band] = bandpasses[band] * atmosphere * quantum_efficiency\n", + " \n", + "bp_atmos_qe['rprime'].plot(left=3000, right=14000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we convolve the source spectrum with all of these effects using synphot's Observation:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from synphot.observation import Observation\n", + "\n", + "band = 'rprime' # only want to know about the r' band\n", + "\n", + "observation = Observation(photlam_hatp11, bp_atmos_qe[band])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 8. Compute the count rate and total counts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the expected count rate for a specific collecting area, use the countrate() method on the observation object: " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "countrate = observation.countrate(area=aperture_area) / gain" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many counts can we expect in a 10s exposure?" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HAT-P-11 counts in the r-band, 10 s exposure:\n", + "38353638 cts\n" + ] + } + ], + "source": [ + "exptime = 10 * u.s\n", + "counts_hatp11 = countrate * exptime\n", + "\n", + "print(\"HAT-P-11 counts in the r-band, 10 s exposure:\")\n", + "print(int(counts_hatp11.value), \"cts\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 9. Signal to noise ratio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To estimate the signal to noise ratio of this observation, input the total counts into `synphot`'s `howell_snr` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR of HAT-P-11 in 10 s exposure:\n" + ] + }, + { + "data": { + "text/latex": [ + "$6193.0315 \\; \\mathrm{ct^{1/2}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from synphot.observation import howell_snr\n", + "\n", + "print(\"SNR of HAT-P-11 in 10 s exposure:\")\n", + "howell_snr(counts_hatp11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also use `synphot` to calculate the exposure time needed to obtain a certain SNR:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposure time needed to obtain SNR > 100:\n" + ] + }, + { + "data": { + "text/latex": [ + "$0.0026073145 \\; \\mathrm{s}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from synphot.observation import exptime_from_howell_snr\n", + "\n", + "desired_snr = 100 * np.sqrt(1 * u.ct)\n", + "\n", + "print(\"Exposure time needed to obtain SNR > 100:\")\n", + "exptime_from_howell_snr(desired_snr, countrate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10. Another example, this time with TRAPPIST-1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "(using the same instrument and telescope)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", + "\n", + "url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", + " 'PHOENIX-ACES-AGSS-COND-2011/Z-0.0/lte{T_eff:05d}-{log_g:1.2f}-0.0.PHOENIX-'\n", + " 'ACES-AGSS-COND-2011-HiRes.fits').format(T_eff=T_eff, log_g=4.5)\n", + "# Unit from PHOENIX model file:\n", + "flux = fits.getdata(url) * (u.erg / u.s / u.cm ** 2 / u.cm)\n", + "\n", + "wavelength_url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", + " 'WAVE_PHOENIX-ACES-AGSS-COND-2011.fits')\n", + "wavelengths = fits.getdata(wavelength_url) * u.Angstrom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately Gaia does not have all the parameters we need for TRAPPIST-1, so we use the values cited in Gillon 2017:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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YsGSL7+esbLILenLO6wYcG+j5TcWS5VkT4x3PxsSKSBowEjgfyAFmi8hYVV0S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\nqo4F3gDecQcP7MRJJLj1xuA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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "photlam_trappist1 = SourceSpectrum(Empirical1D,\n", + " points=wavelengths, lookup_table=flux)\n", + "\n", + "# Scale the flux to get the value at Earth\n", + "stellar_radius = 0.117 * u.R_sun # radius of trappist-1 in solar units\n", + "distance = 12.1 * u.pc # distance to trappist-1 in parsecs\n", + "photlam_trappist1 = photlam_trappist1 * float(stellar_radius / distance) ** 2 / np.pi\n", + "\n", + "photlam_trappist1.plot(flux_unit='Jy', left=0, right=60000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time I just want to know about observations in the z-band:" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", + "306750 cts\n" + ] + } + ], + "source": [ + "band = 'zprime'\n", + "\n", + "observation = Observation(photlam_trappist1, bp_atmos_qe[band])\n", + "\n", + "countrate = observation.countrate(area=aperture_area) / gain\n", + "counts_trappist1 = countrate * 10 * u.s\n", + "\n", + "print(\"TRAPPIST-1 counts in the z-band, 10 s exposure:\")\n", + "print(int(counts_trappist1.value), \"cts\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute the signal to noise ratio:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR of TRAPPIST-1 in 10 s exposure:\n" + ] + }, + { + "data": { + "text/latex": [ + "$553.85044 \\; \\mathrm{ct^{1/2}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"SNR of TRAPPIST-1 in 10 s exposure:\")\n", + "howell_snr(counts_trappist1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can get a more detailed estimate of the signal to noise ratio if you include additional noise sources, such as the readnoise of the detector or the level of background noise to expect:" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR of TRAPPIST-1 in 10 s:\n" + ] + }, + { + "data": { + "text/latex": [ + "$495.79257 \\; \\mathrm{ct^{1/2}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "npix = 50 * u.pixel # number of pixels the object fills\n", + "n_background = 100 * u.pixel # number of pixels in background aperture\n", + "background = 1000 * u.ct / u.pixel # background noise\n", + "readnoise = 3.7 * u.ct / u.pixel # readnoise of ARCTIC from website\n", + "\n", + "print(\"SNR of TRAPPIST-1 in 10 s:\")\n", + "howell_snr(counts_trappist1, npix=npix,\n", + " n_background=n_background,\n", + " background=background,\n", + " readnoise=readnoise)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These specifications can also be made to estimate the exposure time needed to obtain a given SNR (note that you must give the rate of the backround noise):" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposure time needed to obtain SNR > 100:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "//anaconda/lib/python3.5/site-packages/astropy/units/quantity.py:464: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "//anaconda/lib/python3.5/site-packages/scipy/optimize/minpack.py:162: RuntimeWarning: The iteration is not making good progress, as measured by the \n", + " improvement from the last ten iterations.\n", + " warnings.warn(msg, RuntimeWarning)\n" + ] + }, + { + "data": { + "text/latex": [ + "$0.39743843 \\; \\mathrm{s}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from synphot.observation import exptime_from_howell_snr\n", + "\n", + "background_rate = background / exptime\n", + "\n", + "print(\"Exposure time needed to obtain SNR > 100:\")\n", + "exptime_from_howell_snr(desired_snr, countrate,\n", + " npix=npix,\n", + " n_background=n_background,\n", + " background_rate=background_rate,\n", + " readnoise=readnoise)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 11. Predicting photon counts of the same stars, this time observed by a space-based telescope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The space telescope we will use as the example is Kepler.\n", + "\n", + "First query Kepler's response function, which includes both the bandpass and the quantum efficiency:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: W42: /var/folders/bn/d7v8bk_16cjg8qcm1gj03m6h0000gp/T/tmp40avx65c:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f = tynt.FilterGenerator()\n", + "filt = f.download_true_transmittance('Kepler/Kepler.K')\n", + "\n", + "kepler_response = SpectralElement(Empirical1D,\n", + " points=filt.wavelength,\n", + " lookup_table=filt.transmittance)\n", + "\n", + "kepler_response.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the observation object. Since Kepler is a space-based telescope, we do not need to convolve with the atmospheric transmission function:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "stars = {'hatp11': {'spectrum': photlam_hatp11},\n", + " 'trappist1': {'spectrum': photlam_trappist1}\n", + " }\n", + "\n", + "for star in stars:\n", + " stars[star]['observation'] = Observation(stars[star]['spectrum'], kepler_response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute the counts in a for the two stars:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "counts predicted by synphot in 10s exposure:\n", + "\t HAT-P-11: 24.7 million\n", + "\t TRAPPIST-1 65.0 thousand\n" + ] + } + ], + "source": [ + "kepler_area = np.pi * (1.4 * u.m / 2) ** 2 # area of Kepler's primary mirror\n", + "\n", + "for star in stars:\n", + " countrate = stars[star]['observation'].countrate(area=kepler_area)\n", + " stars[star]['counts'] = countrate * 10 * u.s\n", + " \n", + "print('counts predicted by synphot in 10s exposure:')\n", + "print('\\t HAT-P-11:', round(stars['hatp11']['counts'].value / 1e6, 1), 'million')\n", + "print('\\t TRAPPIST-1', round(stars['trappist1']['counts'].value / 1e3, 1), 'thousand')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12. Comparing simulated counts to observed counts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know from real observations (with ARCTIC on APO's 3.5m telescope and with Kepler) that the actual count values for these stars taken on certain dates are:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hatp11_r_apo = 34000000\n", + "hatp11_kepler = 28000000\n", + "trappist1_z_apo = 203000\n", + "trappist1_kepler = 57000" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ground-based counts at Apache Point Observatory:\n", + "\t HAT-P-11 in r-band: 34 million\n", + "\t TRAPPIST-1 in z-band: 203 thousand\n", + "Space-based count rates with Kepler:\n", + "\t HAT-P-11: 28.0 million\n", + "\t TRAPPIST-1 57.0 thousand\n" + ] + } + ], + "source": [ + "print('Ground-based counts at Apache Point Observatory:')\n", + "print('\\t HAT-P-11 in r-band:', int(hatp11_r_apo / 1e6), 'million')\n", + "print('\\t TRAPPIST-1 in z-band:', int(trappist1_z_apo / 1e3), 'thousand')\n", + "print('Space-based count rates with Kepler:')\n", + "print('\\t HAT-P-11:', round(hatp11_kepler / 1e6, 1), 'million')\n", + "print('\\t TRAPPIST-1', round(trappist1_kepler / 1e3, 1), 'thousand')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparing our `synphot` values to the observed values on a 1-1 line:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KgFdeeYXXXnuNSpUqJRcXgE6dOlGiRAl8fHyoWLEiEyZMwMvLi6lTp/Lqq69S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\nTZqkv//97xo5cqTMzHcsAEAevGcGOIZDhw7pllt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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(9, 9))\n", + "\n", + "plt.scatter(hatp11_r_apo, counts_hatp11,\n", + " color='b', s=100, label=\"HAT-P-11, ground\")\n", + "plt.scatter(hatp11_kepler, stars['hatp11']['counts'],\n", + " color='b', marker= '^', s=100, label=\"HAT-P-11, space\")\n", + "plt.scatter(trappist1_z_apo, counts_trappist1,\n", + " color='r', s=100, label='TRAPPIST-1, ground')\n", + "plt.scatter(trappist1_kepler, stars['trappist1']['counts'],\n", + " color='r', marker= '^', s=100, label='TRAPPIST-1, space')\n", + "\n", + "# one-to-one line\n", + "countrange = np.linspace(1e4, 1e8, 20)\n", + "plt.plot(countrange, countrange, color='black')\n", + "\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "plt.ylabel('synphot counts', size='14')\n", + "plt.xlabel('observed counts', size='14')\n", + "\n", + "plt.legend()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "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": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/skymodel.py b/skymodel.py new file mode 100644 index 0000000..b2bd572 --- /dev/null +++ b/skymodel.py @@ -0,0 +1,202 @@ +import os +import json +import requests + +import astropy.units as u +from astropy.io import fits + + +def get_atmospheric_transmittance(airmass=1.0, pwv_mode='pwv', season=0, + time=0, pwv=3.5, msolflux=130.0, + incl_moon='Y', moon_sun_sep=90.0, + moon_target_sep=45.0, moon_alt=45.0, + moon_earth_dist=1.0, incl_starlight='Y', + incl_zodiacal='Y', + ecl_lon=135.0, ecl_lat=90.0, + incl_loweratm='Y', incl_upperatm='Y', + incl_airglow='Y', incl_therm='N', + therm_t1=0.0, therm_e1=0.0, + therm_t2=0.0, therm_e2=0.0, therm_t3=0.0, + therm_e3=0.0, vacair='vac', wmin=300.0, + wmax=2000.0, + wgrid_mode='fixed_wavelength_step', + wdelta=0.1, wres=20000, lsf_type='none', + lsf_gauss_fwhm=5.0, lsf_boxcar_fwhm=5.0, + observatory='paranal'): + """ + Returns the model atmospheric transmittance curve queried from the SkyCalc + Sky Model Calculator. The default parameters used here are the default + parameters provided by SkyCalc: + http://www.eso.org/observing/etc/doc/skycalc/skycalc_defaults.txt + + Parameters + ---------- + airmass: float (range [1.0,3.0]) + Airmass. Alt and airmass are coupled through the plane parallel + approximation airmass=sec(z), z being the zenith distance + z=90°−Alt + pwv_mode: str + options: ['pwv','season'] (default is 'pwv') + season: int + Time of year if not in pwv mode. + options: [0,1,2,3,4,5,6] (default is 0) + (0 = all year, 1 = dec/jan, 2 = feb/mar...) + time: int + Period of night. options: [0,1,2,3] (default is 0) + (0 = all year, 1, 2, 3 = third of night) + pwv: float + Precipitable Water Vapor (default is 3.5). + options: [-1.0,0.5,1.0,1.5,2.5,3.5,5.0,7.5,10.0,20.0] + msolflux: float + Monthly Averaged Solar Flux, s.f.u float > 0 (default is 130.0) + incl_moon: str + Flag for inclusion of scattered moonlight. options = ['Y', 'N'] + (default is 'Y') + Moon coordinate constraints: |z – zmoon| ≤ ρ ≤ |z + zmoon| where + ρ=moon/target separation, z=90°−target altitude and + zmoon=90°−moon altitude. + moon_sun_sep: float + Degrees of separation between Sun and Moon as seen from Earth + (i.e. the "moon phase"). + options: [0.0,360.0] (default is 90.0) + moon_target_sep: float + Moon-Target Separation ( ρ ) + Degrees in range [0.0,180.0] (defualt is 45.0) + # degrees float range [-90.0,90.0] Moon Altitude over Horizon + moon_alt: float + Moon Altitude over Horizon. Degrees in range [-90.0,90.0] + (default is 45.0) + moon_earth_dist: float + Moon-Earth Distance (mean=1) in range [0.91,1.08] + (default is 1.0) + incl_starlight: str + Flag for inclusion of scattered starlight. + options: ['Y', 'N'] (default is 'Y') + incl_zodiacal: str + Flag for inclusion of zodiacal light. + options: ['Y', 'N'] (default is 'Y') + ecl_lon: float + Heliocentric ecliptic in degree range [-180.0,180.0]. + (default is 135.0) + ecl_lat: float + Ecliptic latitude in degree range [-90.0,90.0]. + (default is 90.0) + + incl_loweratm: str + Flag for inclusion of molecular emission of lower atmosphere. + options: ['Y', 'N'] (default is 'Y') + incl_upperatm: str + Flag for inclusion of molecular emission of upper atmosphere. + options: ['Y', 'N'] (default is 'Y') + incl_airglow: str + Flag for inclusion of airglow continuum (residual continuum) + options: ['Y', 'N'] (default is 'Y') + + incl_therm: str + Flag for inclusion of instrumental thermal radiation. + options: ['Y', 'N'] (default is 'N') + Note: This radiance component represents an instrumental effect. + The emission is provided relative to the other model components. + To obtain the correct absolute flux, an instrumental response curve + must be applied to the resulting model spectrum. + See section 6.2.4 in the SkyCalc documentation at + http://localhost/observing/etc/doc/skycalc/ + The_Cerro_Paranal_Advanced_Sky_Model.pdf + therm_t1, therm_t2, therm_t3 : float + Temperature in K (default is 0.0) + therm_e1, therm_e2, therm_e3: float + In range [0,1] (default is 0.0) + + vacair: str + In regards to the wavelength grid. + options: ['vac', 'air] (default is 'vac') + wmin: float + Minimum wavelength (nm) in the wavelength grid. + Must be in range [300.0,30000.0] and < wmax + (default is 300.0) + wmax: float + Maximum wavelength (nm) in the wavelength grid. + Must be in range [300.0,30000.0] and > wmin + (default is 2000.0) + wgrid_mode: str + Mode of the wavelength grid. + options: ['fixed_spectral_resolution','fixed_wavelength_step', 'user'] + (default is 'fixed_wavelength_step') + wdelta: float + Wavelength sampling step dlam in range [0,30000.0] (nm/step) + (default is 0.1) + wres: int + lam/dlam where dlam is wavelength step. + In range [0,1.0e6] (default is 20000) + wgrid_user: list of floats + default is [500.0, 510.0, 520.0, 530.0, 540.0, 550.0] + + lsf_type: str + Line spread function type for convolution. + options: ['none','Gaussian','Boxcar'] (default is 'none') + lsf_gauss_fwhm: float + Gaussian full-width half-max for line spread function wavelength bins. + Range > 0.0 (default is 5.0) + lsf_boxcar_fwhm: float + Boxcar full-width half-max for line spread function wavelength bins. + Range > 0.0 (default is 5.0) + + observatory: str + Observatory where observation takes place. + Options are 'paranal', 'lasilla', 'armazones' (default is 'paranal') + + Returns + ------- + trans_waves, transmission: tuple of arrays of floats + 'trans_waves' is an array of wavelengths in angstroms (float), + 'transmission' is an array of fractional atmospheric + transmittance (float). + + """ + + params = locals() + + if params['observatory'] == 'lasilla': + params['observatory'] = '2400' + elif params['observatory'] == 'paranal': + params['observatory'] = '2640' + elif (params['observatory'] == '3060m' or + params['observatory'] == 'armazones'): + params['observatory'] = '3060' + else: + raise ValueError('Wrong Observatory name, please refer to the ' + 'skycalc_cli documentation.') + + # Use the bit from skycalc_cli which queries from the SkyCalc Sky Model + server = 'http://etimecalret-001.eso.org' + url = server + '/observing/etc/api/skycalc' + response = requests.post(url, data=json.dumps(params)) + results = json.loads(response.text) + + status = results['status'] + tmpdir = results['tmpdir'] + tmpurl = server + '/observing/etc/tmp/' + tmpdir + '/skytable.fits' + + if status == 'success': + try: + response = requests.get(tmpurl, stream=True) + data = response.content + except requests.exceptions.RequestException as e: + print(e, 'could not retrieve FITS data from server') + else: + print('HTML request failed', results) + + # Create a temporary file to write the binary results to + tmp_data_file = './tmp_skycalc_data.fits' + + with open(tmp_data_file, 'wb') as f: + f.write(data) + + hdu = fits.open(tmp_data_file) + trans_waves = hdu[1].data["LAM"] * u.um # wavelengths + transmission = hdu[1].data["TRANS"] + + # Delete the file after reading from it + os.remove(tmp_data_file) + + return trans_waves.to(u.angstrom), transmission