KASI AI Hub KASI AI Hub

Gravitational-wave Electromagnetic Counterpart Korean Observatory (GECKO): GECKO Follow-up Observation of GW190425

Paek et al. (2024)

Changsu Choi #6 Co-author Lee, Chung-Uk #14 Co-author Kim Seung-Lee #15 Co-author Hyun-Il Sung #16 Co-author
ASTROPHYSICAL JOURNAL IF 4.9 A 등급 International collaboration
레몬산천문대 1m 광학망원경KMTNet(외계행성 탐색시스템)

Abstract

One of the keys to the success of multimessenger astronomy is the rapid identification of the electromagnetic wave counterpart, kilonova (KN), of the gravitational-wave (GW) event. Despite its importance, it is hard to find a KN associated with a GW event, due to a poorly constrained GW localization map and numerous signals that could be confused as a KN. Here, we present the Gravitational-wave Electromagnetic wave Counterpart Korean Observatory (GECKO) project, the GECKO observation of GW190425, and prospects of GECKO in the fourth observing run (O4) of the GW detectors. We outline our follow-up observation strategies during O3. In particular, we describe our galaxy-targeted observation criteria that prioritize based on galaxy properties. Armed with this strategy, we performed an optical and/or near-infrared follow-up observation of GW190425, the first binary neutron star merger event during the O3 run. Despite a vast localization area of 7460 deg2, we observed 621 host galaxy candidates, corresponding to 29.5% of the scores we assigned, with most of them observed within the first 3 days of the GW event. Ten transients were discovered during this search, including a new transient with a host galaxy. No plausible KN was found, but we were still able to constrain the properties of potential KNe using upper limits. The GECKO observation demonstrates that GECKO can possibly uncover a GW170817-like KN at a distance <200 Mpc if the localization area is of the order of hundreds of square degrees, providing a bright prospect for the identification of GW electromagnetic wave counterparts during the O4 run.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

classification / clustering

supporting

Techniques

classical-ML

Tasks

classificationanomaly-detection

Subfield

transients/time-domain

tabular-catalog

Flags

Develops AI method
· Classification confidence: 90%