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SkyMapper optical follow-up of gravitational wave triggers: Alert science data pipeline and LIGO/Virgo O3 run
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 $\sim200\,\mathrm{Mpc}$ distance. We describe our robotic facility for rapid transient follow-up...
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| Published in: | Publications of the Astronomical Society of Australia 2021, Vol.38, Article e024 |
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| Main Authors: | , , , , , , , , , |
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| cited_by | cdi_FETCH-LOGICAL-c308t-f6745888a8a972739278b6cd08cee1853b1ee391cc98d27f633be951338663f23 |
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| cites | cdi_FETCH-LOGICAL-c308t-f6745888a8a972739278b6cd08cee1853b1ee391cc98d27f633be951338663f23 |
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| container_title | Publications of the Astronomical Society of Australia |
| container_volume | 38 |
| creator | Chang, Seo-Won Onken, Christopher A. Wolf, Christian Luvaul, Lance Möller, Anais Scalzo, Richard Schmidt, Brian P. Scott, Susan M. Sura, Nikunj Yuan, Fang |
| description | 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
$\sim200\,\mathrm{Mpc}$
distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at
$\delta |
| doi_str_mv | 10.1017/pasa.2021.17 |
| format | article |
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$\sim200\,\mathrm{Mpc}$
distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at
$\delta<+10\deg $
to a depth of
$i_\mathrm{AB}\approx 20\,\mathrm{mag}$
. 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 (
$\sim98\%$
) and purity (
$\sim91\%$
) 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
$i_\mathrm{AB}\approx 21\,\mathrm{mag}$
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$\sim200\,\mathrm{Mpc}$
distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at
$\delta<+10\deg $
to a depth of
$i_\mathrm{AB}\approx 20\,\mathrm{mag}$
. 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 (
$\sim98\%$
) and purity (
$\sim91\%$
) 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
$i_\mathrm{AB}\approx 21\,\mathrm{mag}$
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$\sim200\,\mathrm{Mpc}$
distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at
$\delta<+10\deg $
to a depth of
$i_\mathrm{AB}\approx 20\,\mathrm{mag}$
. 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 (
$\sim98\%$
) and purity (
$\sim91\%$
) 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
$i_\mathrm{AB}\approx 21\,\mathrm{mag}$
.</abstract><cop>New York, USA</cop><pub>Cambridge University Press</pub><doi>10.1017/pasa.2021.17</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-3118-8275</orcidid><orcidid>https://orcid.org/0000-0003-3740-1214</orcidid><orcidid>https://orcid.org/0000-0003-0017-349X</orcidid><orcidid>https://orcid.org/0000-0002-4569-016X</orcidid></addata></record> |
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| source | Cambridge University Press |
| title | SkyMapper optical follow-up of gravitational wave triggers: Alert science data pipeline and LIGO/Virgo O3 run |
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