TY - JOUR
T1 - The IPAC Image Subtraction and Discovery Pipeline for the Intermediate Palomar Transient Factory
AU - Masci, FJ
AU - Laher, RR
AU - Rebbapragada, UD
AU - Doran, GB
AU - Miller, AA
AU - Bellm, E
AU - Kasliwal, M
AU - Ofek, EO
AU - Surace, J
AU - Shupe, DL
AU - Grillmair, CJ
AU - Jackson, E
AU - Barlow, T
AU - Yan, L
AU - Cao, Y
AU - Cenko, SB
AU - Storrie-Lombardi, LJ
AU - Helou, G
AU - Prince, TA
AU - Kulkarni, SR
N1 - iPTF project at the California Institute of Technology; ZTF project at the California Institute of Technology; National Science Foundation [AST-144034]; National Science Foundation PIRE GROWTH award; NASA from Hubble Fellowship grant [HST-HF-51325.01]; STScI; NASA [NAS 5-26555]; NASA This work was funded in part by the iPTF and ZTF projects at the California Institute of Technology. iPTF is a partnership led by the California Institute of Technology and includes the Infrared Processing & Astronomical Center; Los Alamos National Laboratory; University of Wisconsin at Milwaukee; Oskar-Klein Center of the University of Stockholm, Sweden; Weizmann Institute of Sciences, Israel; University System of Taiwan, Taiwan; the Institute for Physics & Mathematics of the universe, Japan; Lawrence Berkeley National Laboratory and the University of California, Berkeley. ZTF is funded by the National Science Foundation under grant no. AST-144034. M.M.K. acknowledges support from the National Science Foundation PIRE GROWTH award. A.A.M. acknowledges support for this work by NASA from a Hubble Fellowship grant: HST-HF-51325.01, awarded by STScI, operated by AURA, Inc., for NASA, under contract NAS 5-26555. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA.
PY - 2017/1
Y1 - 2017/1
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.
U2 - 10.1088/1538-3873/129/971/014002
DO - 10.1088/1538-3873/129/971/014002
M3 - مقالة
SN - 0004-6280
VL - 129
JO - Publications of the Astronomical Society of the Pacific
JF - Publications of the Astronomical Society of the Pacific
IS - 971
ER -