Stage 2 steps

The stage 2 steps perform mode-specific corrections on the rate-images, followed by spectral extraction. Below we detail the workings of several steps that you may find useful for improving the quality of your reduction beyond the default JWST stage 2. For a complete list of ExoTiC-MIRI steps and their workings, see the API.

The following examples assume you have setup the pipelines and loaded in a _rateints.fits data segment. For example:

import os

os.environ["CRDS_SERVER_URL"] = "https://jwst-crds.stsci.edu"
os.environ["CRDS_PATH"] = "path/to/your/local/crds_dir"
os.environ["CRDS_CONTEXT"] = "jwst_1100.pmap"

from jwst import datamodels
from jwst.pipeline import calwebb_spec2

# Load data segment.
proc = datamodels.CubeModel("path/to/your/data/jwxyz-segnnn_mirimage_rateints.fits")

Get the wavelength map

To get the MIRI LRS wavelength map you can run GetWavelengthMap. Note that you must run jwst.calwebb_spec2.assign_wcs_step and jwst.calwebb_spec2.srctype_step before this step.

from exotic_miri.reference import GetWavelengthMap


custom_get_wavelength_map = GetWavelengthMap()
stsci_assign_wcs = calwebb_spec2.assign_wcs_step.AssignWcsStep()
stsci_srctype = calwebb_spec2.srctype_step.SourceTypeStep()

proc = stsci_srctype.call(proc)
sproc = stsci_assign_wcs.call(proc)
wavelength_map = custom_get_wavelength_map.call(proc)

Here we have generated a wavelength_map which represent the mapping from detector pixels (row_idx, col_idx) to wavelength (lambda).

Outlier cleaning

If outliers remain in your rate-images, then this step offers a method for cleaning them without having to use any time-domain information, and proceeds frame-by-frame. Outlier cleaning is performed by finding deviations from an estimated spatial profile of the spectral trace, following Horne 1986. Outliers can also be optionally found by specifying specific data quality flags you wish to expunge. See CleanOutliersStep.

from exotic_miri.stage_2 import CleanOutliersStep


custom_clean_outliers = CleanOutliersStep()
proc, P, O = custom_clean_outliers.call(proc, dq_bits_to_mask=[0, 11],
                                        window_heights=[40],
                                        poly_order=4, outlier_threshold=5.0)

Here, a spatial profile is constructed from fourth-order polynomials, using windows 40 pixels long in the dispersion direction. Outlying pixels are replaced if their values are >5 sigma from the spatial profile or if they have data quality flags 2**0 (do_not_use) or 2**11 (hot pixel). See the data quality flags table in the docs.

Also returned is a 3D array of the fitted spatial profiles, and a count of the number of outliers cleaned within 0-4 pixels of the spectral trace (column index 36). You can try setting draw_cleaning_col=True to make some interactive plots and get a better feel for the cleaning process, and help tailor the parameters to your dataset.

Background subtraction

To perform background subtraction from your rate-images, you can make use of BackgroundSubtractStep.

from exotic_miri.stage_2 import BackgroundSubtractStep


custom_bkg_subtract = BackgroundSubtractStep()
proc = custom_bkg_subtract.call(proc, method="row_wise")

Here, we have used the default background regions either side of the spectral trace and applied a row-wise background subtraction. There more options for estimating the background as a linear function of detector column, or for smoothing over the background. You can adjust also background region as required. NB. the constant method is not recommended for MIRI LRS data.

Extract 1D spectra

To extract a time-series of 1D stellar spectra, using a box aperture, you can make use of Extract1DBoxStep.

from exotic_miri.stage_2 import Extract1DBoxStep


custom_extract1d_box = Extract1DBoxStep()
wv, spec, spec_unc, trace_sigmas = custom_extract1d_box.call(
    proc, wavelength_map, trace_position=36,
    aperture_center=36, aperture_left_width=4, aperture_right_width=4)

Here, a box aperture (top-hat function) is centred on column 36 (nominal for MIRI LRS) and extends 4 pixels in each direction. The total aperture is therefore 9 pixels wide. Note that you must have run the GetWavelengthMap step, so that you may pass the wavelength map as an input. Note that this step returns four outputs: the wavelengths, the time-series spectra, the uncertainties, and a measure of the point source function (PSF) widths.

ExoTiC-MIRI also has an implementation of optimal extraction (Horne 1986). See Extract1DOptimalStep for details.

Align spectra

Often the pointing stability corresponds to the flux stability in your light curves. The x and y position of the spectral trace through time may be used as a diagnostic or decorrelator. To measure the positions, and optionally re-align the spectra, you can use AlignSpectraStep.

from exotic_miri.stage_2 import AlignSpectraStep


custom_align_spectra = AlignSpectraStep()
spec, spec_unc, x_shifts, y_shifts = custom_align_spectra.call(
    proc, spec, spec_unc, align_spectra=False)

Note that this step requires the outputs from Extract1DBoxStep or Extract1DOptimalStep as inputs.