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Active STANDARD GRANT National Science Foundation (US)

WoU-MMA: Constraining and Understanding Extreme Astrophysics, Cosmic Magnetism and Dark Matter

$4.5M USD

Funder National Science Foundation (US)
Recipient Organization New York University
Country United States
Start Date Sep 01, 2024
End Date Aug 31, 2027
Duration 1,094 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2413153
Grant Description

The highest energy particles in the Universe are Ultrahigh Energy Cosmic Rays (UHECRs), whose energies are more than 100,000 times higher than the highest energy particles which humans have been able to produce in our ground-based accelerators. How Nature can produce such high energy particles has long been a major puzzle in astroparticle physics. The PI has recently proposed that UHECRs are produced in binary neutron star (BNS) mergers, which can explain all the numerous constraints on their sources and has not yet been ruled out.

With this award, the PI and her graduate students and collaborators will test the BNS merger hypothesis. They will develop tools to exploit information on individual UHECR composition coming from the upgraded Pierre Auger Observatory. The team has a wide-ranging broader impacts program including public lectures, mentoring of junior researchers at all levels, and investigating how to expand outreach to include older adults.

UHECRs are charged particles and thus deflected in extragalactic magnetic fields, delaying their arrival relative to neutrinos, gamma rays and gravitational waves produced in the same event by hundreds of thousands of years. As well as discovering the origin of these highest energy particle messengers (UHECRS, Very High Energy neutrinos, and gamma rays), this project will characterize in detail the Galactic magnetic field and understand its origin, and make progress identifying Dark Matter and constraining its interactions with ordinary matter.

This will help to backtrack UHECR arrival directions and anisotropies, trusting that the nearest sources may stand out sufficiently over the ensemble contribution to be identifiable. As it is difficult to infer the properties of individual UHECR events, the study includes applying generative AI techniques to tune model parameters more accurately, working with expert collaborators at the Simons Center for Computational Astrophysics.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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New York University

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