WFC3 STAN Issue 39, June 2022
1. WFC3 at AAS 240!
Several WFC3 presentations and iPosters related to WFC3 data analysis and recent WFC3 science results will be featured at the upcoming AAS. We invite all interested users at the AAS to stop by during scheduled sessions to review these projects and their available tools and results. Below, we provide abstracts and session information, as well as link to the individual presentation listings in the AAS itinerary. Session times are given in Pacific Time (PT).
206.01. hst1pass: One-Pass PSF Photometry for HST Detectors - J. Anderson
Tuesday, June 14, 09:00-10:00 AM PT
We are making public and documenting hst1pass: a software routine that has been used for years to find and measure stars in HST images. The hst1pass routine makes use of empirical models of HST’s effective PSF in order to identify and measure relatively isolated stars in images taken with HST’s primary imaging detectors: ACS/WFC, WFC3/UVIS, WFC3/IR, ACS/HRC---and even WFPC2. The user can specify the finding parameters used to search the images as well as the specific measured parameters to be output for each source. The time-averaged “library” PSFs are generally accurate enough for most projects, but the routine has the ability to use focus-diverse PSFs to tailor the PSF to the particular focus of the exposure. Alternatively, the routine can perturb the PSF in an empirical way to improve the photometry. For S/N ~100 stars, the errors in the measured positions are typically 0.01-0.02 pixel and the photometry is generally good to 0.01-0.02 magnitude, and point-source/resolved-source discrimination is quite accurate. The routine has a flexible output format in order to facilitate subsequent collation of outputs from multiple dithered exposures. Future improvements will depend on demand, but could include PSF-convolved Gaussian or 2-source fitting, or even point-source-plus Gaussian fitting.
215.02. Key Improvements in the HST/WFC3 User Experience - J. Green, F. Dauphin, WFC3 Team.
Tuesday, June 14, 10:20-10:30 AM PT
The Hubble Space Telescope (HST) was launched in 1990, but the user community has evolved greatly since then. In the past few years, the number of new HST users has been higher than ever before. The advent of Jupyter notebooks and identified standards of best practice in notebooks has been adopted by the WFC3 team. Here we present the updated Wide Field Camera 3 (WFC3) software landing page, and the new WFC3 Library github repository. Aimed at newer users, these tools provide clear documentation and should be relied upon to source calibration, throughput, and performance files, as well as other supporting documentation for WFC3. We also preview the integration of the HST exposure time calculator (ETC) into the Pandeia engine, similar to JWST.
302.04. HST's Wide Field Camera 3 in 2022 - D. Som, S. Baggett, WFC3 Team.
Wednesday, June 15, 09:00-10:00 AM PT
Wide Field Camera 3 (WFC3), on board the Hubble Space Telescope (HST), provides coverage from the near-UV to the near-IR with direct (staring) and spatial scanning modes using filters and grisms. Installed in 2009, WFC3 continues to be HST's workhorse instrument and has logged almost 300,000 observations, resulting in exciting scientific discoveries over the past 13 years. Here we review the status of the instrument, including recent adjustments and updates to its performance and technical capabilities. We also present the latest calibrations and observing recommendations, along with some late-breaking science enabled by WFC3.
2. Jupyter Notebooks and Software for WFC3 Users
B. Kuhn, J. Green, F. Dauphin
Over the last several months, the WFC3 team has continued to add to our GitHub repository, WFC3Library, which hosts various Python Jupyter notebooks and software tools. Some of the topics include manual calwf3 recalibration, flux conversion, persistence correction, and time-dependent photometry (and many others). The repository includes a virtual environment .yaml file, which once installed and activated, will allow you to run all of the various notebooks and tools. This repository is managed and regularly updated by the WFC3 team, so if you run into any issues or have any questions please contact the WFC3 Help Desk. For more information about all of the WFC3 software tools see the Software Tools Landing Page.
Most recently, we have released a Jupyter notebook that walks through the procedure for accessing data quality, handy not just for general data analysis but also evaluating data from an HST/WFC3 Exception Report to determine if a Hubble Observation Problem Report (HOPR) and/or Help Desk Ticket should be filed. HST observations go through a series of automated data quality checks, and if a problem is found, an Exception Report email will be sent to the Principal Investigator (PI). It is then the responsibility of the PI to assess the data quality and determine whether or not the observations must be repeated to accomplish the science goals. There is a 90-day time limit from the date that the data were delivered to file a HOPR. In this notebook, we provide the steps for directly downloading your data with Astroquery (including observing logs JIF and JIT files), as well as displaying and assessing the files. The analysis process includes viewing the Science, Error, and Data Quality arrays, inspecting the header keywords in the JIF file, plotting the jitter data from the JIT file, and exploring available PSFs for signs of drift. If you are interested in this exception report notebook and/or our other software tools please visit the WFC3Library GitHub repository .
3. DeepWFC3: A Repository for WFC3 Machine Learning Resources
DeepWFC3 is a new repository of machine learning models built using the Hubble Space Telescope's (HST) Wide Field Camera 3 (WFC3) data, and is intended for members of the astronomy community who are interested in implementing machine learning for anomaly detection. Written in Python and built using PyTorch, this repository includes pre-trained weights and biases, data processing scripts, Jupyter Notebooks, and documentation.
Complete models included in this repository are explored in greater detail in the following Instrument Science Reports (ISRs):
- ISR 2021-08: WFC3 IR Blob Classification with Machine Learning
- ISR 2022-03: WFC3/UVIS Figure-8 Ghost Classification using Convolutional Neural Networks
4. New Documentation
Paper accepted to the Astronomical Journal: New Photometric Calibration of the Wide Field Camera 3 Detectors - A. Calamida et al.
ISR 2022-01: Cold and Unstable Pixels in WFC3/IR - H. Khandrika
ISR 2022-02: WFC3/UVIS Encircled Energy - J. Medina, J. Mack, A. Calamida
ISR 2022-03: WFC3/UVIS Figure-8 Ghost Classification using Convolutional Neural Networks - F. Dauphin, M. Montes, N. Easmin, V. Bajaj, P. R. McCullough
View the complete WFC3 ISR archive.
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