FLaapLUC: a pipeline for the generation of prompt alerts on transient Fermi-LAT $\gamma$-ray sources

TL;DR

FLaapLUC is a Python pipeline that detects transient gamma-ray sources in Fermi-LAT data, enabling rapid alerts for follow-up observations and improving transient event detection efficiency.

Contribution

The paper introduces FLaapLUC, a new aperture photometry-based tool for quick detection of gamma-ray transients, complementing traditional likelihood analyses.

Findings

FLaapLUC effectively detects transient gamma-ray events.

It provides rapid alerts for follow-up observations.

The tool's results are comparable to full likelihood analyses.

Abstract

The large majority of high energy sources detected with Fermi-LAT are blazars, which are known to be very variable sources. High cadence long-term monitoring simultaneously at different wavelengths being prohibitive, the study of their transient activities can help shedding light on our understanding of these objects. The early detection of such potentially fast transient events is the key for triggering follow-up observations at other wavelengths. A Python tool, FLaapLUC, built on top of the Science Tools provided by the Fermi Science Support Center and the Fermi-LAT collaboration, has been developed using a simple aperture photometry approach. This tool can effectively detect relative flux variations in a set of predefined sources and alert potential users. Such alerts can then be used to trigger target of opportunity observations with other facilities. It is shown that FLaapLUC is an efficient tool to reveal transient events in Fermi-LAT data, providing quick results which can be used to promptly organise follow-up observations. Results from this simple aperture photometry method are also compared to full likelihood analyses. The FLaapLUC package is made available on GitHub and is open to contributions by the community.