Gender-based Observing Proposal Statistics for the Spitzer Space Telescope
UNKNOWN
Storrie-Lombardi, Lisa J.↗
Engineering topics
Storrie-Lombardi, Lisa J.
Publications and source records attributed to Storrie-Lombardi, Lisa J..
Lessons Learned in Extended-Extended Spitzer Space Telescope Operations
The Spitzer Space Telescope is executing the ninth year of extended operations beyond its 5.5-year prime mission. The project anticipated a maximum extended mission of about four years when the first mission extension was proposed. The robustness of the observatory hardware and the creativity of the project engineers and scientists in overcoming hurdles to operations has enabled a substantially longer mission lifetime. This has led to more challenges with an aging groundsystem due to resource reductions and decisions made early in the extended mission based on a shorter planned lifetime. We provide an overview of the extended mission phases, challenges met in maintaining and enhancing the science productivity, and what we would have done differently if the extended mission was planned from the start to be nearly twice as long as the prime mission.
Lowrance, Patrick↗
The IPAC Image Subtraction and Discovery Pipeline for the Intermediate Palomar Transient Factory
We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, bogus candidates from processing artifacts and imperfect image subtractions outnumber real transients by approximately equal to 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of approximately equal to 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.
methods: analytical – methods: data analysis –↗