Grizli

Grism redshift & line analysis software for space-based slitless spectroscopy

The grizli source code is maintained in a repository at https://github.com/gbrammer/grizli/. Please report any issues there.

What is grizli?

This early release of grizli is intended to demonstrate and demystify some general techniques for manipulating HST slitless spectroscopic observations, providing software kernels to address questions such as

“How does the WFC3/IR G141 grism disperse the spectrum of a star/galaxy at pixel position (x,y) in my F140W direct image?”.

Much of the background related to this question in the context of the currently available software tools was discussed in a document by Brammer, Pirzkal and Ryan (2014). Along with a detailed description of the format of the configuration files originally developed for the aXe software, we provided a compact Python script to address exactly the question above and strip away all of the many layers of bookkeeping, file-IO, etc. in existing analysis pipelines such as aXe (Kummel et al. 2009) and “THREEDHST” (Brammer et al. 2012, Momcheva et al. 2015). In fact, that relatively simple script serves as the low-level kernel for the way grizli computes the grism dispersion.

Eventually, grizli is intended to encourage and enable general users to move away from simple “data reduction” (e.g., extracting a 1D spectrum of a single object akin to standard slit spectroscopy) and toward more quantitative and comprehensive modeling and fitting of slitless spectroscopic observations, which typically involve overlapping spectra of hundreds or thousands of objects in exposures taken with one or more separate grisms and at multiple dispersion position angles. The products of this type of analysis will be complete and uniform characterization of the spectral properties (e.g., continuum shape, redshifts, line fluxes) of all objects in a given exposure taken in the slitless spectroscopic mode.

Warning

Grizli is under active development and significant changes may be made to the algorithms and code structure. Check the commit history and pull the changes from the repository to make sure you’re running with up-to-date bug fixes and feature implementation.

Working Examples

In lieu of detailed documentation for all of the grizli processing beyond information extracted from the docstrings, the following IPython/jupyter notebooks demonstrate various aspects of the code functionality. They render statically in the GitHub pages or can be run locally after cloning and installing the software repository.

  • Grizli-Pipeline : End-to-end processing of WFC3/IR data.
    1. Query the MAST archive and automatically download files
    2. Image pre-processing (astrometric alignment & background subtraction)
    3. Field contamination modeling
    4. Spectral extractions
    5. Redshift & emission line fits (multiple grisms)
  • Fit-with-Photometry : Demonstrate simultaneous fitting with grism spectra + ancillary photometry
  • NewSpectrumFits: Demonstration of the lower-level fitting tools
    1. Unify the fitting tools between the stacked and exposure-level 2D cutouts (“beams”).
  • Fit-Optimization (09.14.17): Custom fitting (hasn’t been tested recently)
    1. Demonstrate some of the workings behind the fitting wrapper scripts by defining custom model functions with parameters to optimize.

The notebooks below are deprecated and haven’t been tested against the master branch since perhaps late 2017.

  • Grizli Demo: Simple interaction with WFC3/IR spectra
  • Basic-Sim (5.5.16): Basic simulations based on single WFC3/IR grism and direct exposures
  • multimission-simulation (5.11.16):
    1. Demonstration of more advanced simulation techniques using deep image mosaics and external catalogs/segmentation images as reference.
    2. Provide a comparison between dispersed spectra from WFC3/G141, JWST/NIRISS and WFIRST.
  • WFC3IR_Reduction (9.6.16): End-to-end processing of WFC3/IR data.
    1. Pre-processing of files downloaded from MAST (astrometric alignment & background subtraction)
    2. Field contamination modeling
    3. Spectral extractions
    4. Redshift & emission line fits (multiple grisms)
  • NIRISS-simulation (11.11.16): Simulation and analysis of JWST/NIRISS observations
    1. Simulate NIRISS spectra in three blocking filters and two orients offset by 90 degrees.
    2. Simulation field taken from the Hubble WFC3/IR Ultra-Deep Field, with the WFC3 F140W image as the morphological reference and photo-z templates taken as the spectral models.
    3. Extract spectra and fit redshifts and emission lines from the combined six exposures.