Multi Order Coverage data structure to plan multi-messenger observations

TL;DR

This paper introduces the use of Multi Order Coverage (MOC) maps for efficient planning and visualization of multi-messenger astronomical observations, especially for gravitational-wave localizations, enhancing interoperability and rapid response capabilities.

Contribution

The paper presents a novel application of MOC maps combined with astroplan for efficient sky region computation and observation planning, improving response times and data accessibility in multi-messenger astronomy.

Findings

MOC maps effectively encode gravitational-wave localizations.

Integration with astroplan enables rapid observation schedule updates.

Demonstrated high interoperability and fast data access for observational campaigns.

Abstract

We describe the use of Multi Order Coverage (MOC) maps as a practical way to manage complex regions of the sky for the planning of multi-messenger observations. MOC maps are a data structure that provides a multi-resolution representation of irregularly shaped and fragmentary regions over the sky based on the HEALPix (Hierarchical Equal Area isoLatitude Pixelization) tessellation. We present a new application of MOC, in combination with the \texttt{astroplan} observation planning package, to enable the efficient computation of sky regions and the visibility of these regions from a specific location on the Earth at a particular time. Using the example of the low-latency gravitational-wave alerts, and a simulated observational campaign with three observatories, we show that the use of MOC maps allows a high level of interoperability to support observing schedule plans. Gravitational-wave detections have an associated credible region localization on the sky. We demonstrate that these localizations can be encoded as MOC maps, and how they can be used in visualisation tools, and processed (filtered, combined) and also their utility for access to Virtual Observatory services which can be queried 'by MOC' for data within the region of interest. The ease of generating the MOC maps and the fast access to data means that the whole system can be very efficient, so that any updates on the gravitational-wave sky localization can be quickly taken into account and the corresponding adjustments to observing schedule plans can be rapidly implemented. We provide example python code as a practical example of these methods. In addition, a video demonstration of the entire workflow is available.