DETECT: A Pipeline to Quantify Detection Thresholds in Rubin for Nearby Targets Embedded in Bright Host Galaxies

TL;DR

DETECT is a pipeline designed to accurately determine detection thresholds for transient sources embedded in bright galaxies, improving the study of pre-supernova variability with upcoming Rubin LSST data.

Contribution

It introduces a novel method combining source injection, image subtraction, and forced photometry to reliably measure detection thresholds in complex backgrounds.

Findings

DETECT effectively identifies pre-SN variability in simulated and real data.

False positives are minimized at SNR above 10.

The pipeline is broadly applicable beyond pre-SN studies.

Abstract

The final stages of stellar evolution can be constrained by studying pre-SN variability. The incredible amount of data coming from the upcoming Rubin Legacy Survey of Space and Time (LSST) will be fundamental to this type of work. However, robustly measuring pre-SN variability can be hard, as even state-of-the-art image subtraction pipelines struggle when the target is embedded in a bright nearby galaxy. We developed Detection Efficiency and Threshold Estimation for Characterization of Transients (DETECT) to tackle this problem. It performs a series of source injection, image subtraction, and forced photometry to obtain reliable detection thresholds tailored to a specific location within a given host galaxy. We first validate the pipeline using simulated data from Rubin DP0 and then apply it to a sample of 15 targets found in Rubin DP1. We demonstrate that DETECT is capable of identifying pre-SN variability while calculating reliable upper limits and suppressing false positives for targets embedded in bright host galaxies. Most of the false positives in this work occurred when the signal-to-noise ratio (SNR) was between 5 and 10, while no false positives were found when the SNR was greater than 10. Finally, even though DETECT was originally developed in the context of pre-SN variability, it is broadly applicable to any situation where detections are uncertain and robust upper limits are needed.