Intermediate Palomar Transient Factory: Realtime Image Subtraction Pipeline

TL;DR

This paper presents a high-speed, real-time image subtraction pipeline for the Palomar Transient Factory that leverages high-performance computing and machine learning to identify transient astronomical events within ten minutes.

Contribution

The paper introduces a novel real-time image subtraction pipeline that efficiently processes large-scale astronomical data using advanced computing and machine learning techniques.

Findings

Transient candidates identified within ten minutes of data acquisition

Pipeline effectively handles big data from time-domain surveys

Demonstrates readiness for future large-scale astronomical data processing

Abstract

A fast-turnaround pipeline for realtime data reduction plays an essential role in discovering and permitting follow-up observations to young supernovae and fast-evolving transients in modern time-domain surveys. In this paper, we present the realtime image subtraction pipeline in the intermediate Palomar Transient Factory. By using high-performance computing, efficient database, and machine learning algorithms, this pipeline manages to reliably deliver transient candidates within ten minutes of images being taken. Our experience in using high performance computing resources to process big data in astronomy serves as a trailblazer to dealing with data from large-scale time-domain facilities in near future.