TY - JOUR
T1 - SkyMapper optical follow-up of gravitational wave triggers
T2 - Alert science data pipeline and LIGO/Virgo O3 run
AU - Chang, Seo Won
AU - Onken, Christopher A.
AU - Wolf, Christian
AU - Luvaul, Lance
AU - Möller, Anais
AU - Scalzo, Richard
AU - Schmidt, Brian P.
AU - Scott, Susan M.
AU - Sura, Nikunj
AU - Yuan, Fang
N1 - Publisher Copyright:
© 2021 Cambridge University Press. All rights reserved.
PY - 2021
Y1 - 2021
N2 - We present an overview of the SkyMapper optical follow-up programme for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-like kilonovae out to 200 Mpc distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at +10deg to a depth of iAB 20 mag. We have implemented a new software pipeline to receive LIGO/Virgo alerts, schedule observations and examine the incoming real-Time data stream for transient candidates. We adopt a real-bogus classifier using ensemble-based machine learning techniques, attaining high completeness (98%) and purity (91%) over our whole magnitude range. Applying further filtering to remove common image artefacts and known sources of transients, such as asteroids and variable stars, reduces the number of candidates by a factor of more than 10. We demonstrate the system performance with data obtained for GW190425, a binary neutron star merger detected during the LIGO/Virgo O3 observing campaign. In time for the LIGO/Virgo O4 run, we will have deeper reference images allowing transient detection to iAB 21 mag.
AB - We present an overview of the SkyMapper optical follow-up programme for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-like kilonovae out to 200 Mpc distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at +10deg to a depth of iAB 20 mag. We have implemented a new software pipeline to receive LIGO/Virgo alerts, schedule observations and examine the incoming real-Time data stream for transient candidates. We adopt a real-bogus classifier using ensemble-based machine learning techniques, attaining high completeness (98%) and purity (91%) over our whole magnitude range. Applying further filtering to remove common image artefacts and known sources of transients, such as asteroids and variable stars, reduces the number of candidates by a factor of more than 10. We demonstrate the system performance with data obtained for GW190425, a binary neutron star merger detected during the LIGO/Virgo O3 observing campaign. In time for the LIGO/Virgo O4 run, we will have deeper reference images allowing transient detection to iAB 21 mag.
KW - gravitational waves
KW - methods: data analysis
KW - methods: statistical
KW - neutron stars
KW - transient detection
UR - http://www.scopus.com/inward/record.url?scp=85106193990&partnerID=8YFLogxK
U2 - 10.1017/pasa.2021.17
DO - 10.1017/pasa.2021.17
M3 - Article
SN - 1323-3580
VL - 38
JO - Publications of the Astronomical Society of Australia
JF - Publications of the Astronomical Society of Australia
M1 - e024
ER -