A Guide to STIS Analysis and Supporting Software
The STIS Data Handbook contains the primary guide to STIS data characteristics and analysis. Requests for STIS data from the HST Archive always return data reprocessed with the current version of CALSTIS and the best available reference files. In many cases, it is optimal to let the archive pipeline reprocess data for you. If standard pipeline processing is inadequate, however, CALSTIS may be downloaded and run locally. Other topics related to STIS data analysis are listed below, including available software packages.
The STIS team manages a public Git repository containing a set of Jupyter Notebooks to aid the STIS user community with observation planning and data analysis, available here under the STScI HST Notebook Repository: https://spacetelescope.github.io/hst_notebooks/. This includes a series of introductory Notebooks, meant to provide walk-through examples of concepts in our Data Handbook. The current list of operational Notebooks is provided below under 'Example and Introductory Notebooks'.
Orbital Ephemeris Files
HST Orbital Ephemeris files are used for correcting observation time events of STIS TIME-TAG data. A tabulated list of all the HST ephemeris files and instruction on how to download is available.
| Software Tool | Purpose |
|---|---|
| STIS Exposure Time Calculators (ETCs) | An online tool that predicts the count rates and S/N in various observing modes given specified source characteristics. STIS has both an Imaging and Spectroscopic ETC. |
| Target Acquisition Simulator (TAS) | A downloadable tool that applies the actual STIS target acquisition algorithm to a user supplied image. |
| Astronomer's Proposal Tool (APT) | The general proposal planning tool for all HST instruments. The Bright Object Tool (BOT) in APT is available for STIS CCD and STIS MAMA observations. |
|
STIS Coronagraphic Visualization Tool (CoronagraphyViz): |
Jupyter Notebook that assists users in planning and preparing STIS coronagraphic observations. |
|
STIS Coronagraphic Observation Feasibility (contrast_sensitivity): |
A complementary notebook to the Coronagraphic Visualization Tool, that acts as a guide to assess the feasibility of high-contrast imaging observations of point sources (i.e. exoplanets, brown dwarf companions) and/or disks around stars for a given expected contrast at the 1, 3 and 5 σ level with STIS coronagraphy. |
| Software Tools | Purpose |
|---|---|
| stistools |
The stistools Python package provides Python-based data processing tools for working with Space Telescope Imaging Spectrograph (STIS) data. It contains the full STIS calibration pipeline as well as its individual components should the user wish to do their calibrations manually. |
| stisblazefix |
The stisblazefix Python package is designed to empirically correct STIS echelle data for misalignment in the blaze function. |
| stis_cti |
The stis_cti Python package implements the Anderson & Bedin (PASP 2010, 122: 1035-1064) pixel-based Charge Transfer Inefficiency (CTI)-correction on HST/STIS CCD data. It performs bias corrections on the data, performs CTI correction on the intermediate products, and finishes running the remaining CalSTIS processing steps. The complete package documentation can be found here. |
| TinyTim |
A tool that generates STIS point spread function (PSF) images. |
| Process | Purpose |
|---|---|
|
Viewing STIS Data (view_data) |
An introductory Jupyter Notebook that walks through different types of STIS data and shows how to access important information in the fits files. It covers how to read in and plot the data, handle the data quality flags, visualize a STIS image, work with TIME-TAG data, and how to access and plot echelle data. |
|
Calstis 2D CCD Calibration Steps (calstis) |
An introductory Jupyter Notebook that provides background for the different calibration steps for the CCD from the raw fits file to the flat fielded (flt) file. This also shows why there is often negative counts (or flux) values in STIS data. The six calibration steps shown are initializing the data quality array, large scale bias and overscale subtraction, small scale bias subtraction, cosmic ray correction, dark signal subtraction, and flat field correction. |
|
1D Spectra Extraction (extraction) |
An introductory Jupyter Notebook that shows how to visualize the 1-D extraction. This is useful for cases where a user may want to do a custom extraction or background subtraction. It shows how to find the important keywords and plot the extraction and background regions used for the extraction to generate x1d data. The notebook contains an example with a first order spectrum and with echelle data. |
|
Custom CCD Darks (custom_ccd_dark) |
An introductory Jupyter Notebook showing how to create a custom CCD dark reference file by making the baseline dark and then the week dark using the refstis package. |
|
Evaluating STIS Target Acquisitions (target_acquisition) |
An introductory Jupyter Notebook that shows how to visualize and obtain information about a target acquisition. It provides examples for a successful acquisition and several typical failure cases. |
|
Correcting for Missing Wavecals with Cross-Correlation (cross-correlation) |
A complementary Jupyter Notebook to the Acquisition Notebook, that shows how to find and correct the zero point spectral shift using cross-correlation. |
|
Defringing a G750M Observation: View Defringing a G750L Observation: View |
STIS CCD observations with the G750M and G750L modes often experience optical fringes in wavelengths greater than 7500 Angstroms. In order to perform the best science possible, these fringes must be divided out. The downloadable notebooks to the left are example workflows for using the defringe routines in stistools to remove unwanted fringing effects in both G750M and G750L data. To learn more about the routines used, check the stistools defringe documentation as well as section 3.5.5 of the STIS Data Handbook . |
|
STIS DrizzlePac Tutorial (drizpac_notebook) |
Jupyter Notebook for aligning and combining STIS images with DrizzlePac. |
|
Low Count Uncertainties in STIS (low_count_uncertainties) |
A Jupyter Notebook exploring how uncertainties are calculated in the STIS pipeline. This also shows how certain approximations break down in the low flux regime (e.g., dim FUV continua), and demonstrates how users can calculate more robust uncertainties when dealing with low flux data. Lastly, this explores a known bug in calculation of uncertainties when using INTTAG to split exposures into sub-exposures in TIME-TAG files. |
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