The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory

TL;DR

The paper details the design, algorithms, and performance of the IPAC/iPTF Discovery Engine, a real-time system for transient source detection and classification in astronomical imaging, emphasizing machine learning effectiveness and false-positive mitigation.

Contribution

It introduces a comprehensive transient detection pipeline with machine learning vetting, achieving high efficiency and low false-positive rates, and discusses system optimization for multiple science goals.

Findings

ML classifier achieves ~97% efficiency at 1% false-positive rate.

High contamination (~10:1) from artifacts can be mitigated with ML.

System stores extensive metadata for diverse science applications.

Abstract

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.