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Identifying Gravitational Wave Counterparts with Near-infrared Image Subtraction: Automating the Detection of GW Counterparts for VISTA

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posted on 2022-08-15, 12:48 authored by Skye Sonja Rosetti

The field of gravitational wave observational astronomy has only just begun, with the first binary black hole merger detected in 2015 and the first electromagnetic counterpart associated to a GW event being found in 2017. With the recent introduction of new entries into the detector network (e.g. Virgo, KAGRA), the planned expansion of the network (e.g. LISA) and with frequent improvements being made to existing detectors, the number of GW detections is set to increase in the coming years. This will encourage additional electromagnetic follow-up, particularly in the infra-red in the search for kilonovae.

This thesis is framed around the primary motivation for this work – to automate the process of investigating near-infrared follow up images obtained from the VISTA telescope. This involves both the identification of potential transient objects which might be associated with an event, and the photometric analysis of known kilonova detections. To demonstrate the effectiveness of this work, the automated pipeline is tested against data obtained within the localisation regions of GW170814, GW190814 and GW200114. A revised estimate is also produced for GW170817 photometry which is in accordance with previous studies.

Overall, the IGNIS (Identifying Gravitational wave counterparts with near Infrared Image Subtraction) pipeline is proven to be almost as effective as manually searching for transient objects to depths of mAB = 20.5 - 22, with automated detection improved on longer exposures (240s+). This work estimates VISTA-associated false positive rates at 0.048-0.06 objects per tile (one object found per 16.7-20.8 deg2). 

History

Supervisor(s)

Nial Tanvir

Date of award

2022-06-16

Author affiliation

Department of Physics and Astronomy

Awarding institution

University of Leicester

Qualification level

  • Doctoral

Qualification name

  • PhD

Language

en

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