Professor Kaeli Hughes's $729K NSF Grant, WoU-MMA: Cosmic Ray and Background Identification with RNO-G, has just received funding!

Abstract from the NSF:
"Neutrinos are unique messenger particles that can carry information about some of the most energetic astrophysical sources in the Universe. The IceCube Neutrino Observatory (ICNO) has very successfully detected astrophysical neutrinos, and discovered sources that expand our knowledge of physics at very high energy scales. There is more to discover, but a larger detector is needed to discover neutrinos at even higher energies. Measuring both cosmic rays and neutrinos at extreme energies will answer questions about both particle physics and astrophysics. This is the purpose of the Radio Neutrino Observatory in Greenland (RNO-G) which looks for the flash of radio emission caused by an extremely energetic neutrino slamming into glacial ice. RNO-G?s full detector will cover dozens of square kilometers; at present, seven of 35 stations are taking data, and more are being built every summer. The greatest challenge is to classify and reject radio backgrounds, especially signals created by cosmic ray particles. This work involves critical simulation and classification development for RNO-G, efficiently separating out the neutrino signal. Building on knowledge from other fields, including machine learning and glaciology, the tools developed for this project will improve our ability to detect neutrinos with RNO-G. The included education and outreach efforts will make this research accessible to a broad range of scientists and to the general public.

Over the course of the project, the team at Ohio State will compare leading time-domain classification techniques and build a library of analysis tools. This work will: (1) develop high-end simulated models including neutrino- and cosmic ray-induced radio emission, (2) classify the flux of cosmic rays into in-air and in-ice categories, likely detecting in-ice Askaryan emission for the first time, and (3) use machine learning to categorize backgrounds with much higher efficiency. The team will conduct both data-driven and simulation-driven studies of cosmic ray signals, comparing to neutrino simulations to optimize classifiers, and thus maximize the separation between neutrinos and backgrounds. Developed tools will enable a future successful neutrino search. This study will transform RNO-G into a true neutrino observatory for energies above 10 Pev, perhaps even above 1 Eev.

This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments.

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."

You can read more on the NSF website.

Congratulations, Professor Hughes!