TY - JOUR
T1 - The IPAC image subtraction and discovery pipeline for the intermediate palomar transient factory
AU - Masci, Frank J.
AU - Laher, Russ R.
AU - Rebbapragada, Umaa D.
AU - Doran, Gary B.
AU - Miller, Adam A.
AU - Bellm, Eric
AU - Kasliwal, Mansi
AU - Ofek, Eran O.
AU - Surace, Jason
AU - Shupe, David L.
AU - Grillmair, Carl J.
AU - Jackson, Ed
AU - Barlow, Tom
AU - Yan, Lin
AU - Cao, Yi
AU - Cenko, S. Bradley
AU - Storrie-Lombardi, Lisa J.
AU - Helou, George
AU - Prince, Thomas A.
AU - Kulkarni, Shrinivas R.
N1 - Publisher Copyright:
© 2016. The Astronomical Society of the Pacific. All rights reserved.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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 ≃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 ≃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.
AB - 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 ≃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 ≃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.
KW - Methods: analytical
KW - Methods: data analysis
KW - Methods: statistical
KW - Techniques: image processing
KW - Techniques: photometric
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U2 - 10.1088/1538-3873/129/971/014002
DO - 10.1088/1538-3873/129/971/014002
M3 - Article
AN - SCOPUS:85006427450
VL - 129
JO - Publications of the Astronomical Society of the Pacific
JF - Publications of the Astronomical Society of the Pacific
SN - 0004-6280
IS - 971
M1 - 014002
ER -