Observing a galaxy with MICADO_Sci in wide-field MCAO mode

In this example we will generate a basic galaxy object with the help of the ScopeSim_templates package. We will then create a model of the ELT+MAORY+MICADO optical system using the MICADO_Sci package. The MICADO_Sci package has been optimised for speed so that the user can rapidly iterate on their science case.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from astropy import units as u

import scopesim as sim
import scopesim_templates as sim_tp

%matplotlib inline

# set to where you want the instrument packages to be located
sim.rc.__config__["!SIM.file.local_packages_path"] = "../../../"

Download the relevant scopesim instrument packages

We will need the MICADO_Sci package, as well as the support packages for Armazones, the ELT, and MAORY. If you have not yet downloaded the packages, you can use the download_package command:

sim.download_package(["locations/Armazones.zip",
                      "telescopes/ELT.zip",
                      "instruments/MAORY.zip",
                      "instruments/MICADO_Sci.zip",])

To view all available packages, use the sim.list_packages command

sim.list_packages(location="local")   # ["all", "local", "server"]

Set up on-sky source object

For ease of use we will use the helper function from ScopeSim_templates to create a two-layer spiral galaxy object. The Source object contains two image layers (in <Source>.fields) that each reference a unique spectrum. The two layers correspond to the old and the new stellar populations of the galaxy.

gal = sim_tp.extragalactic.galaxies.spiral_two_component(extent=16*u.arcsec, fluxes=(15, 15)*u.mag)

Here we can see what is contained in the .fields and .spectra lists of the Source object

wave = np.linspace(0.3, 2.5, 2201) * u.um
plt.figure(figsize=(15, 10))

plt.subplot(221)
plt.imshow(gal.fields[0].data)
plt.subplot(222)
plt.plot(wave, gal.spectra[0](wave))
plt.subplot(223)
plt.imshow(gal.fields[1].data)
plt.subplot(224)
plt.plot(wave, gal.spectra[1](wave))

Set up the MICADO_Sci system for MCAO 4mas J-band observations

The next step is to create a model of MICADO in memory. First we need to generate a set of commands that we can manipulate. Once we have these we can create a model of the optical system.

We use the UserCommands class and tell it to look for the MICADO_Sci package. At the same time we set the observing modes to use. In this case we want MCAO and the 4mas per pixel wide-field optics (IMG_4mas)

cmd = sim.UserCommands(use_instrument="MICADO_Sci", set_modes=["SCAO", "IMG_4mas"])

We can update many of the observation parameters using the “!-bang” string syntax to access the values inside the hierarchical nested-dictionary structure of the UserCommands object.

Here we set the filter to the J-band, exposure length (DIT) to 60 seconds and number of exposures (DIT) to 1. We can pass these parameters either as a dictionary using the .update(properties={...}) method, or individually using normal dictionary notation with the “!-bang” strings.

cmd.update(properties={"!OBS.filter_name": "J", "!OBS.ndit": 1, "!OBS.dit": 60})

cmd["!DET.width"] = 2048     # pixel
cmd["!DET.height"] = 1024

Now that we have the commands set, we create the MICADO optical model

micado = sim.OpticalTrain(cmd)

Observing and reading out the detectors is also a simple operation

micado.observe(gal)
hdus = micado.readout()

The output of a simulation is a list of fits.HDUList objects. One HDUList for each detector plane. In MICADO there is only 1 detector plane, but the class interface still requires that this single HDUList be placed in another list.

We can view the output of our custom Detecotr window like we would for any regular fits image in Python

Alternatively we could save the readout file to disk with the parameter filename=:

micado.readout(filename="my_galaxy.fits")

plt.figure(figsize=(10,10))
plt.imshow(hdus[0][1].data, norm=LogNorm())