from matplotlib import pyplot as plt
import numpy as np
from astropy import units as u
METIS AIT activities in Leiden will use bespoke relay optics to observe the sky above the integration hall with METIS. Scopesim can simulate these observations for the spectroscopic modes. Emission and transmission spectra for the sky above Leiden were calculated by Wolfgang Kausch for 50 values of precipital water vapour (PWV). The data are stored on the Astar server in Vienna and will be downloaded the first time you use them.
import scopesim as sim
# Edit this path if you have a custom install directory, otherwise comment it out.
sim.link_irdb("../../../../")
# If you haven't got the instrument packages yet, uncomment the following line.
#sim.download_packages(["METIS"])
The available modes using the Leiden sky are leiden_lss_l, leiden_lss_m, leiden_lss_n and leiden_lms. We will simulate an LSS_M observation, using a PWV value of 10 mm.
cmd = sim.UserCommands(use_instrument="METIS", set_modes=['leiden_lss_m'])
cmd["!ATMO.pwv"] = 10 # could also use properties={"!ATMO.pwv": 10} in UserCommands
metis = sim.OpticalTrain(cmd)
metis.effects.pprint_all()
The leiden_ modes replace the ELT and Armazones packages with two effects, leiden_sky, which creates the background source using the atmospheric spectra for the selected PWV, and telescope, which models the relay optics.
telescope has no user-modifiable parameters; its configuration can be inspected as follows:
metis['telescope'].table
The atmospheric emission is provided by the effect leiden_sky. It has one parameter, the precipitable water vapour:
print(metis["leiden_sky"])
The transmission and emission spectra can be plotted as follows:
metis['leiden_sky'].plot(which="te", wavelength=np.linspace(3, 6, 1001)*u.um);
An observation is done as usual with the .observe() method. No Source object is required.
metis.observe()
plt.imshow(metis.image_planes[0].data, norm="log")
plt.plot(metis.image_planes[0].data[:, 1000])
plt.xlabel("Row number (~ wavelength)")
plt.ylabel("Photons/second (ImagePlane)");
The plots show the ImagePlane. After a readout, the metis[spectral_traces].rectify_traces() method could be used to show the simulation on a linear wavelength scale.
Not yet: The leiden_sky effect (or rather the underlying AtmoLibraryTERCurve effect) has an update method, which allows to change the PWV value. This does not work yet! We recommend creating a new OpticalTrain after setting cmd["!ATMO.pwv"] to a new value. This will hopefully be fixed soon.
metis['leiden_sky'].update(pwv=50)
metis['leiden_sky'].plot(which="te", wavelength=np.linspace(3, 6, 1001)*u.um);
Comparison to Armazones/ELT#
The following compares the Leiden spectrum with a spectrum of the Armazones sky and METIS at the ELT.
cmd_elt = sim.UserCommands(use_instrument="METIS", set_modes=["lss_m"])
elt = sim.OpticalTrain(cmd_elt)
elt.observe()
plt.plot(elt.image_planes[0].data[:, 1000]/(36.9**2 - 10.95**2) * (0.66**2 * np.cos(np.pi/4)), label="Armazones")
#plt.plot(elt.image_planes[0].data[:, 1000] / 975.2347899768369 * 0.34, label="Armazones 2")
plt.plot(metis.image_planes[0].data[:, 1000], label="Leiden")
plt.xlabel("Row number (~ wavelength)")
plt.ylabel("Photons/second (ImagePlane)")
#plt.ylim(0, 10)
plt.legend();
leiden = metis['leiden_sky']
armazones = elt["skycalc_atmosphere"]
lam = np.linspace(4, 5, 1001)*u.um
f_leiden = leiden.emission(lam)
f_armazones = armazones.emission(lam)
plt.plot(lam, f_armazones, label="Armazones", lw=1)
plt.plot(lam, f_leiden, label="Leiden", lw=1)
#plt.semilogy()
plt.xlabel("Wavelength [um]")
plt.ylabel("PHOTLAM")
plt.xlim(4.6, 4.8)
plt.legend();
lam_leiden = leiden.surface.emission.waveset.to(u.um)
lam_armazones = armazones.surface.emission.waveset.to(u.um)
plt.plot(lam_armazones, armazones.surface.emission(lam_armazones), label="Armazones")
plt.plot(lam_leiden, leiden.surface.emission(lam_leiden), label="Leiden")
plt.xlim(3.2, 3.4)
plt.ylim(0, 0.5)
plt.legend()
rlam_leiden = lam_leiden[(lam_leiden > 4.6*u.um) * (lam_leiden < 4.8*u.um)]
leiden.emission.integrate(wavelengths=rlam_leiden)
rlam_arm = lam_armazones[(lam_armazones > 4.6*u.um) * (lam_armazones < 4.8*u.um)]
armazones.emission.integrate(wavelengths=rlam_arm)
As expected, the Leiden sky is brighter (in the M band) than the sky above Armazones. Note that this is just an exemplary comparison, and we have not tried to match the atmospheric parameters in much detail. A similar exercise in the L band results in Armazones being brighter than Leiden. It should be borne in mind that the Leiden sky model only includes atmospheric emission and neglects scattered moon light, zodiacal light and other effects that are taken into account in the more sophisticated skycalc model used for Armazones.