Simulating Transmission and Emission Spectroscopy

Simulate exoplanet transmission and/or emission spectroscopy without using the coronagraph routines. This uses the same telesope and detector parameters as the coronagraph model, but does not suppress the star’s light. As a result, stellar photons dominate the noise budget.

For transmission spectroscopy calculations use TransitNoise, and for emission spectroscopy use EclipseNoise. You may also get an example transmission and emission spectrum of the Earth by calling get_earth_trans_spectrum().

class coronagraph.transits.EclipseNoise(tdur=3432.0, telescope=<coronagraph.teleplanstar.Telescope object>, planet=<coronagraph.teleplanstar.Planet object>, star=<coronagraph.teleplanstar.Star object>, ntran=1, nout=1, wantsnr=1000.0, NIR=True, THERMAL=True, GROUND=False, vod=False, IMAGE=False)

Simulate exoplanet secondary eclipse emission spectroscopy with a next-generation telescope.

Parameters

  • telescope (Telescope) – Initialized object containing Telescope parameters

  • planet (Planet) – Initialized object containing Planet parameters

  • star (Star) – Initialized object containing Star parameters

  • tdur (float) – Transit duration [s]

  • ntran (float) – Number of transits/eclipses

  • nout (float) – Number of out-of-eclipse transit durations to observe

  • wantsnr (float, optional) – Desired signal-to-noise ratio in each pixel

  • FIX_OWA (bool, optional) – Set to fix OWA at OWA*lammin/D, as would occur if lenslet array is limiting the OWA

  • COMPUTE_LAM (bool, optional) – Set to compute lo-res wavelength grid, otherwise the grid input as variable lam is used

  • SILENT (bool, optional) – Set to suppress print statements

  • NIR (bool, optional) – Re-adjusts pixel size in NIR, as would occur if a second instrument was designed to handle the NIR

  • THERMAL (bool, optional) – Set to compute thermal photon counts due to telescope temperature

  • GROUND (bool, optional) – Set to simulate ground-based observations through atmosphere

  • vod (bool, optional) – “Valley of Death” red QE parameterization from Robinson et al. (2016)

run_count_rates(lamhr=None, Fphr=None, Fshr=None)

Calculate the photon count rates and signal to noise on a secondary eclipse spectrum observation

Parameters

  • lamhr (numpy.ndarray) – Wavelength [$mu$m]

  • Fphr (numpy.ndarray) – Dayside exoplanet TOA flux spectrum [W/m$^2$/$mu$]

  • Fshr (numpy.ndarray) – Stellar flux incident at the planet’s TOA [W/m$^2$/$mu$]

  • run_count_rates() creates the following attributes for (Calling) –

  • EclipseNoise instance (the) –

Variables

  • lamhr (array) – Wavelength [$mu$m]

  • Fphr (array) – Dayside exoplanet TOA flux spectrum [W/m$^2$/$mu$]

  • Fshr (array) – Stellar flux incident at the planet’s TOA [W/m$^2$/$mu$]

  • cs (array) – Stellar photon count rate [photons/s]

  • cback (array) – Background photon count rate [photons/s]

  • cz (array) – Zodi photon count rate [photons/s]

  • cez (array) – Exo-zodi photon count rate [photons/s]

  • cth (array) – Thermal photon count rate [photons/s]

  • cD (array) – Dark current photon count rate [photons/s]

  • cR (array) – Read noise photon count rate [photons/s]

  • cmiss (array) – Occulted stellar photon count rate [photons/s]

  • SNR1 (array) – S/N for one eclipse

  • SNRn (array) – S/N for ntran eclipses

  • tSNR (array) – Exposure time to wantsnr [s]

  • nSNR (array) – Number of eclipses to wantsnr

  • lam (array) – Observed wavelength grid [$mu$m]

  • dlam (array) – Observed wavelength grid widths [$mu$m]

  • FpFslr (array) – Low-res planet/star flux ratio

  • FpFshr (array) – High-res planetr/star flux ratio

make_fake_data()

Make a fake dataset by sampling from a Gaussian.

Variables

  • SNRn (array) – S/N in ntran eclipses

  • obs (array) – Observed emission specrum with noise

  • sig (array) – Observed uncertainties on emission spectrum

recalc_wantsnr(wantsnr=None)

Recalculate the time and number of eclipses required to achieve a user specified SNR via wantsnr.

Variables

  • tSNR (array) – Exposure time to wantsnr [s]

  • nSNR (array) – Number of eclipses to wantsnr

plot_spectrum(SNR_threshold=0.0, Nsig=None, ax0=None, err_kws={'alpha': 1, 'c': 'k', 'fmt': '.'}, plot_kws={'alpha': 0.5, 'c': 'C4', 'lw': 1.0}, draw_box=True)

Plot noised emission spectrum.

Parameters

  • SNR_threshold (float) – Threshold SNR below which do not plot

  • Nsig (float) – Number of standard deviations about median observed points to set yaxis limits

  • ax0 (matplotlib.axes) – Optional axis to provide

  • err_kws (dic) – Keyword arguments for errorbar

  • plot_kws (dic) – Keyword arguments for plot

  • draw_box (bool) – Draw important quantities in a box?

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

plot_SNRn(ax0=None, plot_kws={'ls': 'steps-mid'})

Plot the S/N on the Eclipse Depth as a function of wavelength.

Parameters

  • ax0 (matplotlib.axes) – Optional axis to provide

  • plot_kws (dic) – Keyword arguments for plot

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

plot_ntran_to_wantsnr(ax0=None, plot_kws={'alpha': 1.0, 'ls': 'steps-mid'})

Plot the number of eclipses to get a SNR on the eclipse depth as a function of wavelength.

Parameters

  • ax0 (matplotlib.axes) – Optional axis to provide

  • plot_kws (dic) – Keyword arguments for plot

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

plot_time_to_wantsnr(ax0=None, plot_kws={'alpha': 1.0, 'ls': 'steps-mid'})

Plot the time to get a SNR on the eclipse depth as a function of wavelength.

Parameters

  • ax0 (matplotlib.axes) – Optional axis to provide

  • plot_kws (dic) – Keyword arguments for plot

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

plot_count_rates(ax0=None)

Plot the photon count rate for all sources.

Parameters

ax0 (matplotlib.axes) – Optional axis to provide

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

class coronagraph.transits.TransitNoise(tdur=3432.0, telescope=<coronagraph.teleplanstar.Telescope object>, planet=<coronagraph.teleplanstar.Planet object>, star=<coronagraph.teleplanstar.Star object>, ntran=1, nout=1, wantsnr=1000.0, NIR=True, THERMAL=True, GROUND=False, vod=False, IMAGE=False)

Simulate exoplanet transit transmission spectroscopy with a next-generation telescope.

Parameters

  • telescope (Telescope) – Initialized object containing Telescope parameters

  • planet (Planet) – Initialized object containing Planet parameters

  • star (Star) – Initialized object containing Star parameters

  • tdur (float) – Transit duration [s]

  • ntran (float) – Number of transits

  • nout (float) – Number of out-of-transit transit durations to observe

  • wantsnr (float, optional) – Desired signal-to-noise ratio in each pixel

  • FIX_OWA (bool, optional) – Set to fix OWA at OWA*lammin/D, as would occur if lenslet array is limiting the OWA

  • COMPUTE_LAM (bool, optional) – Set to compute lo-res wavelength grid, otherwise the grid input as variable lam is used

  • SILENT (bool, optional) – Set to suppress print statements

  • NIR (bool, optional) – Re-adjusts pixel size in NIR, as would occur if a second instrument was designed to handle the NIR

  • THERMAL (bool, optional) – Set to compute thermal photon counts due to telescope temperature

  • GROUND (bool, optional) – Set to simulate ground-based observations through atmosphere

  • vod (bool, optional) – “Valley of Death” red QE parameterization from Robinson et al. (2016)

run_count_rates(lamhr=None, tdhr=None, Fshr=None)

Calculate the photon count rates and signal to noise on a transmission spectrum observation

Parameters

  • lamhr (numpy.ndarray) – Wavelength [$mu$m]

  • tdhr (numpy.ndarray) – Transit Depth $(Rp/Rs)^2$

  • Fshr (numpy.ndarray) – Flux density incident at the planet’s TOA [W/m$^2$/$mu$]

  • run_count_rates() creates the following attributes for (Calling) –

  • TransitNoise instance (the) –

Variables

  • lamhr (array) – Wavelength [$mu$m]

  • tdhr (array) – Transit Depth $(Rp/Rs)^2$

  • Fshr (array) – Flux density incident at the planet’s TOA [W/m$^2$/$mu$]

  • cs (array) – Stellar photon count rate [photons/s]

  • cback (array) – Background photon count rate [photons/s]

  • cz (array) – Zodi photon count rate [photons/s]

  • cez (array) – Exo-zodi photon count rate [photons/s]

  • cth (array) – Thermal photon count rate [photons/s]

  • cD (array) – Dark current photon count rate [photons/s]

  • cR (array) – Read noise photon count rate [photons/s]

  • cmiss (array) – Occulted stellar photon count rate [photons/s]

  • SNR1 (array) – S/N for one transit

  • SNRn (array) – S/N for ntran transits

  • tSNR (array) – Exposure time to wantsnr [s]

  • nSNR (array) – Number of transits to wantsnr

  • lam (array) – Observed wavelength grid [$mu$m]

  • dlam (array) – Observed wavelength grid widths [$mu$m]

  • RpRs2 (array) – Low-res transit depth

make_fake_data()

Make a fake dataset by sampling from a Gaussian.

Variables

  • SNRn (array) – S/N in ntran transits

  • obs (array) – Observed transit depth with noise

  • sig (array) – Observed uncertainties on transit depth

recalc_wantsnr(wantsnr=None)

Recalculate the time and number of transits required to achieve a user specified SNR via wantsnr.

Variables

  • tSNR (array) – Exposure time to wantsnr [s]

  • nSNR (array) – Number of transits to wantsnr

plot_spectrum(SNR_threshold=1.0, Nsig=6.0, ax0=None, err_kws={'alpha': 1, 'c': 'k', 'fmt': '.'}, plot_kws={'alpha': 0.5, 'c': 'C4', 'lw': 1.0}, draw_box=True)

Plot noised transmission spectrum.

Parameters

  • SNR_threshold (float) – Threshold SNR below which do not plot

  • Nsig (float) – Number of standard deviations about median observed points to set yaxis limits

  • ax0 (matplotlib.axes) – Optional axis to provide

  • err_kws (dic) – Keyword arguments for errorbar

  • plot_kws (dic) – Keyword arguments for plot

  • draw_box (bool) – Draw important quantities in a box?

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

plot_SNRn(ax0=None, plot_kws={'ls': 'steps-mid'})

Plot the S/N on the Transit Depth as a function of wavelength.

Parameters

  • ax0 (matplotlib.axes) – Optional axis to provide

  • plot_kws (dic) – Keyword arguments for plot

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

plot_ntran_to_wantsnr(ax0=None, plot_kws={'alpha': 1.0, 'ls': 'steps-mid'})

Plot the number of transits to get a SNR on the transit depth as a function of wavelength.

Parameters

  • ax0 (matplotlib.axes) – Optional axis to provide

  • plot_kws (dic) – Keyword arguments for plot

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

plot_time_to_wantsnr(ax0=None, plot_kws={'alpha': 1.0, 'ls': 'steps-mid'})

Plot the time to get a SNR on the transit depth as a function of wavelength.

Parameters

  • ax0 (matplotlib.axes) – Optional axis to provide

  • plot_kws (dic) – Keyword arguments for plot

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

plot_count_rates(ax0=None)

Plot the photon count rate for all sources.

Parameters

ax0 (matplotlib.axes) – Optional axis to provide

Returns

  • fig (matplotlib.figure.Figure) – Returns a figure if ax0 is None

  • ax (matplotlib.axes) – Returns an axis if ax0 is None

Note

Only returns fig and ax is ax0 is None

coronagraph.transits.get_earth_trans_spectrum()

Get the transmission spectrum of the Earth around the Sun.

Returns

  • lam (numpy.ndarray) – Wavelength grid [um]

  • tdepth (numpy.ndarray) – Transit depth (Rp/Rs)^2

  • fplan (numpy.ndarray) – TOA planet flux [W/m^2/um]

  • fstar (numpy.ndarray) – Stellar flux at planet [W/m^2/um]