# Capability of Detecting Ultra-Violet Counterparts of Gravitational Waves   with GLUV

**Authors:** R. Ridden-Harper, B. Tucker, R. Sharp, J. Gilbert, M. Petkovic

arXiv: 1706.06106 · 2017-09-05

## TL;DR

This paper explores the potential of a dedicated UV survey telescope to detect electromagnetic counterparts of gravitational wave events, emphasizing its design requirements and the scientific benefits of such observations.

## Contribution

It proposes a design for a small, balloon-based UV telescope capable of detecting UV counterparts of GW sources, filling a gap in current multi-messenger astronomy capabilities.

## Key findings

- A UV survey telescope with m_{u'} ≈ 24 can complement aLIGO's detection range.
- A network of 30 cm balloon-based UV telescopes could cover up to 100% of BNS events.
- UV emission sensitivity to initial conditions offers a diagnostic tool for models.

## Abstract

With the discovery of gravitational waves (GW), attention has turned towards detecting counterparts to these sources. In discussions on counterpart signatures and multi-messenger follow-up strategies to GW detections, ultra-violet (UV) signatures have largely been neglected, due to UV facilities being limited to SWIFT, which lacks high-cadence UV survey capabilities. In this paper, we examine the UV signatures from merger models for the major GW sources, highlighting the need for further modelling, while presenting requirements and a design for an effective UV survey telescope. Using $u'$-band models as an analogue, we find that a UV survey telescope requires a limiting magnitude of m$_{u'}\rm (AB)\approx 24$ to fully complement the aLIGO range and sky localisation. We show that a network of small, balloon-based UV telescopes with a primary mirror diameter of 30~cm could be capable of covering the aLIGO detection distance from $\sim$60--100\% for BNS events and $\sim$40\% for BHNS events. The sensitivity of UV emission to initial conditions suggests that a UV survey telescope would provide a unique dataset, that can act as an effective diagnostic to discriminate between models.

## Full text

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## Figures

11 figures with captions in the complete paper: https://tomesphere.com/paper/1706.06106/full.md

## References

64 references — full list in the complete paper: https://tomesphere.com/paper/1706.06106/full.md

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Source: https://tomesphere.com/paper/1706.06106