The ZTF-ULTRASAT experiment: Characterizing the non-transients in ULTRASAT's high cadence survey

TL;DR

This study uses the ZTF-ULTRASAT experiment to analyze high-cadence observations, identifying variable stars that can mimic transients in UV surveys and proposing strategies to mitigate this contamination.

Contribution

It presents a novel approach combining high-cadence optical observations with machine learning to characterize and reduce contamination in UV transient searches.

Findings

Five persistent variables and two spurious sources identified among candidates.

Short-period RR Lyrae stars and flaring sources can mimic UV transients.

A strategy using machine learning catalogs to mitigate false transient alerts.

Abstract

The forthcoming launch of the Ultraviolet Transient Astronomy Satellite (ULTRASAT) will transform our understanding of the transient ultraviolet sky by increasing our ability to identify transients due to its unprecedented 204 deg2 field of view. While rapid (extragalactic) transients are a priority science area for the mission, flaring stars and AGN can often contaminate searches for such objects. To prepare for these challenges, the Zwicky Transient Facility (ZTF)-ULTRASAT experiment observed five fields at high cadence over three nights, in close proximity to ULTRASAT's three northern high-cadence fields. A real-time filter identified seven transient candidates, of which five were persistent variable sources and two were spurious. Periods and amplitudes derived from the ZTF Source Classification Project (SCoPe) showed that three candidates were RR Lyrae stars with short periods and high amplitudes, while the remaining two displayed flaring behavior. We demonstrate that short-timescale, high-amplitude variables can systematically mimic transient alerts in high-cadence UV surveys, and we provide a concrete strategy to this contamination using pre-existing machine learning catalogs.