JWST Post-Pipeline Data Analysis
The JWST post-pipeline data analysis ecosystem consists of several components (i.e., analysis software, visualization tools, and Jupyter notebooks) that can be integrated for seamless viewing and analyzing of JWST data.
JWST post-pipeline data analysis tools are individual packages included within AstroConda to assist observers in viewing and analyzing their JWST data. The tools are generally written in Python and work with Astropy. Development is ongoing1.
The suite of post-pipeline data analysis tools is intended to help astronomers with the often iterative and interactive workflow involved in converting these calibration pipeline data products into meaningful scientific results. This involves tasks such as:
- inspecting data and data quality information;
- masking or flagging data and using those annotations to guide later steps in the analysis;
- using the results of interactive analysis to guide a custom run of the pipeline (e.g., tweaking spectral extraction parameters or background estimates);
- performing optimized spectral extraction techniques;
- combining data sets in various ways, with careful attention to astrometry, PSF matching, and other issues;
- source detection and photometry using different choices or algorithms than those used in the pipeline;
- measuring lines and continuum in spectral data;
- fitting models to data or otherwise testing hypotheses.
A typical workflow involves highly interactive exploratory analysis on small portions of the data, followed by the development of custom scripts to automate the analysis on larger data sets. Online training is available via the JWebbinar series.
1 All software is open source and community contributions are welcome in the form of suggestions, bug reports, or actual code. Further details on how to contribute can be found at the Data Analysis Tools Development Forum.
See also: JWebbinar Landing Page
Beginning in Spring 2021, STScI will be hosting JWebbinars to teach the JWST community about tools and methods to analyze data from the James Webb Space Telescope. Each JWebbinar will provide virtual, hands-on instruction on common data analysis methods for JWST observations. All tutorials will be recorded and resources will be made available to the community online at a later date.
JWebbinar topics will include:
- Overview of data analysis tools and data products
- The JWST pipeline for imaging
- The JWST pipeline for spectroscopy
- Analyzing IFUs and spectral cubes
- Analyzing MIRI photometry
- Analyzing time series spectroscopy
Previous user training workshops are archived an available for proposers to review, too. These are not as up-to-date as the JWebbinar series.
Table 1 provides links to further information about the tools.
Table 1. Software tools information
A community python library for astronomy
A Python library to explore relationships within and among related datasets
A toolkit designed for building viewers for scientific image data in Python
Tools for detecting and performing photometry of astronomical sources
|specutils||Tools for performing spectroscopic analysis of astronomical sources||GitHub||ReadTheDocs|
Convenience tools for working with astronomical images
Tool for simple image examination, and plotting, with similar functionality to IRAF's imexamine
An interactive analysis tool, including Specviz, Cubeviz, and Mosviz:
Advanced Scientific Data Format is a next generation interchange format for scientific data
Generalized World Coordinate System tools for dealing with image and spectral geometries
Synthetic photometry toolkit for building model spectra and estimating count-rates
See Also: Jdaviz Landing Page
The JWST Data Analysis Visualization (Jdaviz) Tool is a specific component of the broader analysis tool ecosystem designed to work seamlessly with astropy within a Jupyter notebook. The visualization tools also may be operated as standalone desktop applications or as embedded windows within a website. The tool currently has three configurations:
- Specviz is a tool for visualization and quick-look analysis of 1D astronomical spectra.
- Mosviz is a visualization tool for viewing multi-object spectrograph data (e.g., JWST NIRSpec MOS mode), and includes viewers for 2D and 1D spectra as well as contextual information like on-sky views of the spectrograph slit.
- Cubeviz provides access to and manipulation of spectroscopic data cubes (like those produced by JWST MIRI MRS or NIRSpec IFU), along with 1D spectra extractions from the cube.
The Jdaviz tools have demonstration videos available:
JWST Jupyter notebooks
A series of Jupyter notebooks have been created to illustrate workflows for analyzing JWST data. The notebooks utilize astropy machinery, including the jdaviz visualization tools described above in Table 1. The notebooks are likely to be useful for analyzing data from other observatories as well. These notebooks can be downloaded and executed by cloning the GitHub repository to your local computer. Most of the notebooks rely on packages that are available in astroconda, although a few rely on packages that should be installed using pip. The version dependencies are listed in the
environment.yaml and in the
requirements file in each notebook folder. You can also just view rendered versions of the notebooks.
Table 2. JWST Jupyter notebooks
|Specviz GUI Interaction|
|MultiBand Aperture Photometry|
|Crowded Field Aperture Photometry|
|PSF (Matched) Photometry|
|WFSS Galaxy Extraction and Analysis|
|SOSS Transiting Exoplanet|
|NIRISS AMI Binary Star|
|IFU Analysis (Continuum Fitting)|
|MOS Optimal Extraction|
|MOS Pre-Imaging w/ NIRCam|
|BOTS Transiting Exoplanet|
|IFU Optimal Extraction|
|IFU Cube Fitting|
|LRS Optimal Extraction|
|MRS IFU Cube Analysis 1|
|MRS IFU Cube Analysis 2|
Astropy is a community library, and the JWST Analysis Tools' success relies on community members, like you, to engage in the development process via bug reports (most effectively filed as github issues, but the JWST help desk is fine as well), or by code contributions through github pull requests. Use of the development versions of the code straight from github comes with the following caveats: at any given time, the code may not actually run or return correct results, and the documentation may be inconsistent with the code. Users who are not interested in contributing to the development software should use the versions in AstroConda.