Sanjay Reddy
Institute for Nuclear Theory

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Kimberlee Choe
(206) 685-3509

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INT-JINA Symposium

First multi-messenger observations of a neutron star merger
and its implications for nuclear physics

March 12 - 14, 2018

Observation of gravitational waves (GWs), gamma-rays, x-rays, optical, infrared and radio waves from a neutron star (NS) merger event, now called GW170817, has the potential to revolutionize nuclear astrophysics. Data from this event has already provided strong hints that heavy elements are produced in NS mergers, and that these elements directly influence the observed optical and infra-red light curves. Properties of dense matter which was expected to play a key role also appear to be essential in interpreting the GW data.

An unprecedented observing campaign, triggered within hours of the discovery in GWs by Advanced LIGO & VIRGO, and in gamma-rays by Fermi, resulted in EM and GW data with detailed spectral and temporal features. This wealth of new data has arrived at the most opportune time. Advances in nuclear astrophysics, nuclear theory and computational astrophysics in recent years that led to the development of simulation and analysis tools that have played a critical role in the interpretation of the multi- messenger data from GW170817.

In the coming months, collaborative efforts involving nuclear physicists, computational astrophysicists and the observing (GW and EM) communities will continue to sharpen the interpretation, and likely identify puzzling discrepancies. The INT has already played an instrumental role by presciently bringing together these communities. Results and discussions during recent INT programs on Binary Neutron Star Coalescence as a Fundamental Physics Laboratory, and Nucleosynthesis and Chemical Evolution in 2014, and Electromagnetic Signatures of R-process Nucleosynthesis in Neutron Star Binary Mergers, July 24 - August 18, 2017, enabled the early interpretation of data from GW170817. This symposium plans bring together participants from these past programs and others at this remarkable time when we have more data than could have been anticipated.