Exploring the transient Universe in the multi-messenger era: Planning, Assessment and Observation

We present a new application to enable the efficient computation of sky regions and the visibility of these regions in the cases where astrophysical sources may be localized in relatively large sky regions, and the planning cannot be done point by point.

This occurs quite frequently in the transient multimessenger astronomy with gravitational-wave sky localizations, error boxes of the short GRBs and neutrino candidate detections.

We describe a practical approach that uses Multi-Order Coverage (MOC) maps to describe regions on the sky that are observable from given locations on the Earth, taking into account specific constraints on the airmass values and the time allocated for the observation.

Operationally, this approach uses Python modules for the creation and manipulation of MOCs (mocpy) and for use of the HEALPix tessellation (cds-healpix-python), combined with the astroplan package to select the HEALPix indices at a given order and to set up a new MOC map that represents the visibility with all of these constraints taken into account.

The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the AHEAD2020 project (grant agreement n. 871158).

This work has been partly supported by the ESCAPE project (the European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures) that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement n. 824064.

The scheduling of observational follow-up programs requires many considerations to be taken into account. The primary consideration is the choice of the sky  fields which is determined by the credible regions.

Here we discuss other issues which also affect the observing strategy: (i) removing high Galactic dust extinction areas, and (ii) identifying regions in which reference images are already available.

The Einstein Telescope (ET) is a proposed underground infrastructure to host a third-generation, gravitational-wave observatory. It builds on the success of current, second-generation laser-interferometric detectors Advanced Virgo and Advanced LIGO, whose breakthrough discoveries of merging black holes (BHs) and neutron stars over the past 6 years have ushered scientists into the new era of gravitational-wave astronomy.  

The Einstein Telescope will achieve a greatly improved sensitivity by increasing the size of the interferometer from the 3km arm length of the Virgo detector to 10km, and by implementing a series of new technologies. These include a cryogenic system to cool some of the main optics to 10 – 20K, new quantum technologies to reduce the fluctuations of the light, and a set of infrastructural and active noise-mitigation measures to reduce environmental perturbations.

ET will explore the universe with gravitational waves up to cosmological distances with an expected detection  rate of order 10⁵−10⁶ black holes and  about 10⁴ neutron stars mergers per year. 

Fast and real time data access could be provided by encoding the ET sky localizations into ST–MOC and querying them from a specific time range. Visibility access can be provided by the Visibility MOC. To query the ST-MOC by a time ranges can offer an interoperability benefit with  network of electromagnetic/neutrino facilities and ET detections.

We recreate the MOC observabilities of the gravitational-wave sky localization of GW190425.

The original sky map was previously processed taking into account the all-sky Galactic reddening map from  and overlapping the PanSTARRS DR1 survey as reference images. The visibility refers to three astronomical observatories: Haleakala Observatories in Hawaii (USA), Paranal Observatory in Chile and Siding Spring Observatory (SSO) in Australia. The time interval is defined from 08:18:05 UTC to 14:18:05 UTC in two hour steps with airmass 1 < X < 2.

The Table below lists the overlap areas in square degrees. As indicated by the blank values in the second column  there are no simultaneous overlaps between the three observatories at any time, but columns 3 and 4 show significant intersections between Haleakala and Paranal, and Haleakala and SSO. The generations of these simple tables allow to organize strategies among several observers.

Continuing the illustration of this example, to optimize the observations planning in such case, the intersection areas can be totally subtracted from Haleakala schedula and redistributed between Paranal and SSO observatories.

We apply the method to identify observable sky zone in a MOC map in the context of the low-latency gravitational-wave alert of GW190425. The event represents the discovery of a second binary neutron star merger after GW170817. The GW190425 signal has been observed on 2019 April 25, 08:18:05 UTC, during the third observing run (O3) of the LIGO and Virgo network. The network consists of two Advanced LIGO interferometers in Hanford, Washington, USA (LHO) and Livingston, Louisiana, USA (LLO) and the Advanced Virgo interferometer in Cascina, Italy. At the time of GW190425, LHO was temporarily o ine with only LLO and Virgo taking data. The initial sky map, generated by BAYESTAR algorithm, has a 90% credible region of 10,183 deg². Up to the present day, no confirmed electromagnetic or neutrino event has been identi ed in association with this gravitational-wave event. This system is notable for having a total mass that exceeds that of known galactic neutron star binaries.

A simulated observational campaign is performed with three observatories.

1) Haleakala Observatories in Hawaii, USA

2) Paranal Observatory in Chile

3) Siding Spring Observatory (SSO) in Australia

The MOC (Multi Order Coverage) encoding method was originally developed at the Centre de Donneés astronomiques de Strasbourg (CDS) and has been adopted as a recommendation (i:e: standard) by the International Virtual Observatory Alliance (IVOA).

Initially designed for manipulating sky coverages from astronomical surveys, MOC has been extended to support both temporal and spatial coverage, known as Space-Time MOC (ST-MOC).

MOCs are a very useful mechanism to improve the efficiency of querying of VO services. A number of services support queries based on MOCs (e.g. the CDS VizieR service). VO services may also have their coverage maps included in the MOC Server, which can help by providing a way to limit queries only to services who have coverage in the sky region of interest.

Such considerations help optimise the response times of data searches which can be critical in planning of observations.

1. B. P. Abbott, R. Abbott, T. D. Abbott, et al., GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral, Phys. Rev. 445 Lett. 119 (2017) 161101. doi:10.1103/PhysRevLett.119.161101. URL https://link.aps.org/doi/10.1103/PhysRevLett.119.161101.

2. B. P. Abbott, R. Abbott, T. D. Abbott, et al., GW190425: Observation of a Compact Binary Coalescence with Total Mass  3.4  solar masses, The Astrophysical Journal (1) (2020) L3. doi:10.3847/2041-8213/ab75f5. URL https://doi.org/10.3847/2041-8213/ab75f5.

3. The LIGO Scientific Collaboration and the Virgo Collaboration, GCN CIRCULAR 24168: LIGO/Virgo S190425z: Identification of a GW compact binary merger candidate, https://gcn.gsfc.nasa.gov/gcn3/24168.gcn3 (2019).

4. P. Fernique, A. Nebot, D. Durand, M. Baumann, T. Boch, G. Greco, T. Donaldson, F.-X. Pineau, M. Taylor, W. O'Mullane, M. Reinecke, S. Derri ere, MOC: Multi-Order Coverage map Version 2.0. IVOA Working Draft 12 November 2020, https://www.ivoa.net/documents/MOC/20201112/index.html (2020).

5. T. Boch, M. Baumann, et al., MOCPy 0.8.4 documentation, https://cds-astro.github.io/mocpy/ (2020).

6. F.-X. Pineau, M. Baumann, et al., cdshealpix 0.5.3 documentation, https://cds-astro.github.io/cds-healpix-python/ (2020).

7. B. M. Morris, E. Tollerud, B. Sip}ocz, C. Deil, S. T. Douglas, J. Berlanga Medina, K. Vyhmeister, T. R. Smith, S. Littlefair, A. M. Price-Whelan, W. T. Gee, E. Jeschke, astroplan: An Open Source Observation Planning Package in Python, The Astronomical Journal 155 (3) (2018) 128. arXiv:1712.09631, doi:10.3847/1538-3881/aaa47e.

8.  L. P. Singer, et al., ligo.skymap 0.5.0 documentation, https://lscsoft.docs.ligo.org/ligo.skymap/ (2020).

9.  M. Punturo, M. Abernathy, F. Acernese, et al., The Einstein Telescope: a third-generation gravitational wave observatory, Classical and Quantum Gravity 27 (19) (2010) 194002. doi:10.1088/0264-9381/27/19/194002.

10. M. Maggiore, C. V. D. Broeck, N. Bartolo, E. Belgacem, D. Bertacca, M. A. Bizouard, M. Branchesi, S. Clesse, S. Fo a, J. Garc  a-Bellido, S. Grimm, J. Harms, T. Hinderer, S. Matarrese, C. Palomba, M. Peloso, A. Ricciardone, M. Sakellariadou, Science case for the einstein telescope, Journal of Cosmology and Astroparticle Physics 2020 (03) (2020) 050{050.doi:10.1088/1475-7516/2020/03/050. URL https://doi.org/10.1088/1475-7516/2020/03/050