CLASSI Spectrograph pipeline

Data levels

L0 - "raw" ICS product

L0 is produced by the instrument-control system in essentially real time. This pipeline does not create L0 products. An L0 product is the detector FITS image with the instrument/observation metadata injected by the ICS, but without any science-pipeline processing.

L1 - detector processing and extraction

L1 consumes an L0 FITS image and produces one-dimensional spectra in detector pixel coordinates. It currently supports:

• optional bias subtraction
• optional dark subtraction
• optional flat correction
• propagated image uncertainty through those corrections using CCDData and ccdproc
• optimal extraction of traces using specreduce

L1 does not perform wavelength calibration or spectrophotometric calibration.

Each extracted trace is written as a binary table HDU with the columns:

• PIXEL — zero-based detector dispersion coordinate;
• COUNTS — extracted detector-domain signal;
• SIGMA — 1-sigma uncertainty in the same unit as COUNTS;
• MASK — invalid/bad spectral samples.

The primary header records PROCLVL=1, which detector corrections were applied, and explicitly records WAVECAL=F and FLUXCAL=F.

L2 - physical spectral calibration

L2 will consume L1 products and be responsible for the physical calibration of the spectra, including:

• wavelength solution and wavelength-coordinate assignment;
• instrumental response / sensitivity correction;
• atmospheric-extinction correction as appropriate;
• spectrophotometric flux calibration;
• later calibration-related operations such as telluric treatment if desired.

The L2 function is stubbed out in the framework but is not implemented yet. A future L2 table can retain PIXEL for provenance while adding a physical WAVELENGTH coordinate and calibrated FLUX/uncertainty columns.

Current L1 assumptions

• Dispersion is along array axis 1 (horizontal/X).
• Traces are currently flat and their Y centers are supplied explicitly or as a bundle center plus regular spacing.
• Seven traces are the default, but the count is configurable with --n-traces.
• Each trace is extracted from its own local cross-dispersion cutout so nearby traces do not contaminate specreduce's spatial-profile fit.
• Trace cross-talk is not modeled. Each trace is extracted independently with a Gaussian spatial profile and a fixed zero background term.
• Calibration images are assumed to already be master products. A flat supplied here should represent detector/pixel-response correction rather than a spectrophotometric response function. Master-frame uncertainty is not (yet) read or propagated.
• A dark used with --dark-scale should already be bias-subtracted and must carry the exposure-time keyword specified by --exposure-key.

Install

python -m pip install -e .

The installed import namespace is pipeline, and installation provides the classi-pipeline command.

L1 usage

With no detector corrections:

classi-pipeline l1 science_l0.fits science_l1.fits \
    --center 1023.5 \
    --spacing 50

With explicit trace centers and detector calibration frames:

classi-pipeline l1 science_l0.fits science_l1.fits \
    --bias master_bias.fits \
    --dark master_dark.fits \
    --dark-scale \
    --flat master_flat.fits \
    --centers 434.9,460.6,486.3,512.0,537.7,563.4,589.1

A provisional variance model can be initialized from gain and read noise:

classi-pipeline l1 science_l0.fits science_l1.fits \
    --center 512.0 \
    --spacing 25.7 \
    --gain 1.2 \
    --read-noise 3.5

In the future, the L2 interface will be called as:

classi-pipeline l2 science_l1.fits science_l2.fits