About This Article
1. ACS Handbooks Updates
R. Lucas, J. Ryon, T. Desjardins, & the ACS Team
New versions of both the ACS Data and Instrument Handbooks (versions 9.0 and 17.0, respectively) have been published in anticipation of HST Cycle 27. These handbooks contain useful information about the ACS instrument performance, engineering specifications, data formats, and the calibration pipeline. In general, both handbooks have been changed to provide better clarity about the information presented, and figures showing changes over the lifetime of ACS have been updated with recent values.
Recent Data Handbook changes include, but are not limited to:
- Replacement of IRAF/PyRAF examples with Python 3 code (except aXe for grism analysis)
- Updated discussion of ACS data processing in MAST after deprecation of on-the-fly reprocessing (OTFR) and the switch to a static archive
- Description of changes to data quality flags for unstable and sink pixel flagging
- Discussion of updates to the pixel-based CTE correction
Recent Instrument Handbook changes include, but are not limited to:
- Discussion of three new ACS/WFC detector anomalies called “flecks”
- The new method of superdark reference file creation and pixel stability flagging
- Sink pixels identification and flagging
- Updates to the pixel-based CTE correction algorithm
- ACS/SBC dark current stability and spatial variation
- New encircled energy values for all channels
- Clarification on the use of acs_destripe_plus for subarray data
- Updates to the post-flash reference file
2. Changes to CALACS
M. De La Pena, N. Miles, & J. Noss
Several important updates to CALACS have been made in recent years. The list is too exhaustive to completely describe here, and users are encouraged to check the release notes on the HSTCAL GitHub repository for detailed information about changes made in each release.
The most user-impactful updates to CALACS over the last several years have been:
- Creation of the updated pixel-based CTE correction (see Section 3 of this STAN). In addition, the pixel-based CTE correction algorithm now uses readout amplifier-dependent read noise values.
- Inclusion of the CTE forward model (ACS/WFC readout simulator).
- Implementation of the
SINKCORRstep using the sink pixel (SNKCFILE) reference file to mark pixel in the data quality arrays that are affected by excessive charge trapping. - Use of the
OSCNTABreference file to correctly define regions for bias level subtraction during theBLEVCORRstep. The change to using this reference file has necessitated the need to freeze user-defined subarray data from future automatic reprocessing in MAST. The ACS Team has delivered recently calibrated versions of these data for archiving. - Bug fixes in the
ACSREJalgorithm for CR-SPLIT observations. Improved cosmic-ray rejection has been implemented inACSREJ.
3. Implementation of an Improved Pixel-based CTE correction
J. Anderson & J. Ryon
ACS CCDs suffer from charge transfer efficiency (CTE) loss over time due charge traps created by radiation damage suffered in low-Earth orbit. Charge is lost as pixels are read out resulting in sources having trails in the y-direction. The pixel-based CTE algorithm is intended to correct for this effect by modeling the charge loss based upon a suite of parameters including time-dependency, background level, source brightness, and position on the detector. The ACS pixel-based CTE correction algorithm was first implemented in 2010, and has now been updated with several times the data that was available at that time. The improved CTE correction was added to the ACS data pipeline in July 2017 and all MAST products that have CTE correction enabled have been reprocessed to use this latest version. More information about the updates made to the pixel-based CTE correction algorithm can be found in ACS ISR 2018-04.
4. ACS/WFC Reference File Updates
D. Borncamp, J. Ryon, S. Hoffmann, & T. Desjardins
The data quality and error arrays of the ACS/WFC superbias reference files have been updated. In the error arrays, the contribution of dark current accumulated during readout has been added as a linear ramp. The variance of this “readout dark” increases with time and is of the order 6 electrons2 at present. In addition, only stable hot columns in the superbias are included in the error arrays. A stable hot column is defined as one that varies over the anneal less than a threshold defined by the observed and expected variance of the pixels in the column. Columns that exceed this stability metric are considered unstable and are flagged in the superbias data quality arrays with value 128 (“bias structure”). More information about changes to the superbias reference files can be found in ACS ISR 2017-13.
Similar to the hot column stability described above, the stability of pixels in ACS/WFC superdark reference files has been examined. Pixels that vary by more than a critical threshold during a given ~4-week annealing interval are now considered unstable and are flagged with value 32 (“unstable pixel”) in the superdark data quality arrays. The cadence of superdark reference file deliveries has been reduced to one per ~4-week annealing interval, and previous superdarks have been recreated to match this frequency. More information about the pixel stability flagging can be found in ACS ISR 2017-05 .
With the changes described above, the ACS Team no longer recommends rejecting hot pixels (data quality flag 16) during image combination with AstroDrizzle. Instead, ACS/WFC data should be combined while rejecting only unstable pixels (flag 32). To support this recommended change, a new MDRIZTAB was delivered in August 2017 that contained updates to several AstroDrizzle parameters to improve photometry on MAST drizzled products. Included in the new MDRIZTAB was a new optimization for the popular 4-point dither pattern. More information about the new MDRIZTAB reference file can be found in ACS ISR 2017-02.
The sink pixel reference file has been added. Some pixels in the ACS/WFC CCDs have been found to have a large number of charge traps that cause these pixels to have anomalously low values compared to the background. In addition, sink pixel can affect nearby pixels in the same column: enhanced pixel values in the readout directions, and conversely suppressed pixel values opposite the readout direction. After each anneal, a sink pixel reference file is created by examining the distribution of pixel values in short (0.5 second) flashed darks. Within CALACS, the sink pixel reference file (SNKCFILE) is used to update the data quality array with flag 1024 (“sink pixel” or pixels affected by a sink pixel). More information about sink pixel file generation can be found in ACS ISR 2017-01 .
5. New ACS/SBC LODARK Aperture
R. Avila
At low operating temperatures, the dark current in the ACS/SBC MAMA detector is negligible. The temperature of the MAMA increases with extended use however, and the temperature-dependent dark current becomes significant at temperatures in excess of 25 °C. CALACS does not remove SBC dark current, and no SBC superdark reference file is available for dark current subtraction. The ACS Team has published a worked example of manual dark-subtraction for SBC observations affected by excessive dark current Section 6 in this STAN). Beginning with APT v26.2, observers concerned about the impact of dark current in SBC observations may now select the SBC-LODARK aperture to place targets in a region of low dark current of the SBC detector. See ACS ISR 2018-07.
6. New ACS Tutorials Available
T. Desjardins, M. Olaes, R. Avila, & J. Ryon
In an effort to assist users who may be unfamiliar with ACS data, the ACS Team has created examples of select topics in the form of Python Jupyter notebooks. These examples were often either previously part of the ACS Data Handbook, located on various ACS webpages, or described in instrument science reports (ISRs). Now they are collected in a GitHub repository for ease of access and in a form that is easily updated based upon community feedback. Jupyter notebooks themselves combine narrative text, workable code, and visualizations that allow users to step through the examples on their local computers and examine the intermediate data products.
Six new notebooks are now available in the acs-notebook Github repository. The following topics are covered:
- Using CALACS to re-reduce raw data
- How to de-stripe and calibrate ACS/WFC subarray observations
- Creating pixel area maps (PAMs) on-the-fly with Python
- Correcting ACS/SBC data for excessive dark current
- Simulating CTE-loss in ACS/WFC images with the CTE forward model
- Performing photometry on full-well saturated stars in ACS observations
Additional examples may be added in the future. For assistance with these notebooks or other ACS-related topics, please submit a ticket to the HST Help Desk .
7. New DrizzlePac Tutorials Available
J. Mack, S. Hoffmann, R. Avila, V. Bajaj, M. Cara, T. Desjardins, C. Martlin, & C. Shanahan
Improved drizzling tutorials are now available as Jupyter Notebooks and compatible with the latest STScI distributed software as part of AstroConda. Prior drizzling examples were written for the DrizzlePac Handbook in 2012, just after MultiDrizzle was replaced, and supplemental examples were posted to the DrizzlePac webpage in 2015 to support enhanced features in DrizzlePac 2.0. The new interactive notebooks consolidate information from these prior examples to form a more cohesive set, and any references to outdated software, such as PyRAF, have been removed and replaced with python functionality.
The notebooks contain live code and visualizations, along with the usual narrative text, making them an ideal training exercise for new users. Each tutorial includes blocks of code demonstrating how to download the calibrated data from the MAST archive, how to align frames and update the image world coordinate system, and how to enhance the scientific value of the drizzled data products using advanced reprocessing techniques.
Ten new notebooks are now available from the DrizzlePac directory in the STScI Notebooks GitHub repository and include the following topics:
- Initializing DrizzlePac
- Aligning observations obtained in different HST visits
- Aligning HST images to an absolute reference catalog (e.g. GAIA, SDSS)
- Aligning sparse fields
- Improving alignment with DS9 exclusion regions
- Masking satellite trails in DQ arrays prior to drizzling
- Optimizing the image sampling for dithered datasets
- Drizzling WFPC2 data to use a single zeropoint
- Sky matching features for HST mosaics
- Aligning HST mosaics observed with multiple detectors
Additional tutorials will be added to this repository as new software functionality becomes available, especially for advanced use cases. For additional assistance with DrizzlePac tools, users may submit a ticket to the HST Help Desk .