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.
@article{arxiv.1608.01733,
title = {The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory},
author = {Frank Masci and Russ Laher and Umaa Rebbapragada and Gary Doran and Adam Miller and Eric Bellm and Mansi Kasliwal and Eran Ofek and Jason Surace and David Shupe and Carl Grillmair and Ed Jackson and Tom Barlow and Lin Yan and Yi Cao and S. Bradley Cenko and Lisa Storrie-Lombardi and George Helou and Thomas Prince and Shrinivas Kulkarni},
journal= {arXiv preprint arXiv:1608.01733},
year = {2016}
}