Oscar Macias Ramirez
San Francisco State University, CA·Astronomy & Astrophysics
Most of what we know about the universe comes from light, from microwaves to gamma rays. At the very highest energies, though, light cannot travel freely across space.
- Fields
Astronomy & Astrophysics
Physics
Computer Science
- Topics
ultra-high-energy neutrinos
UHE neutrinos
Giant Radio Array for Neutrino Detection
- Grants
- 1 known grant, through Jul 2027
What they work on
- Build a fast, accurate parameter regression framework using modern optimization algorithms to reconstruct the particle air showers that ultra-high-energy neutrinos create in the atmosphere.
- Configure and train a convolutional denoising autoencoder, a neural network that removes noise, to separate real radio signals from background noise in GRAND antenna data.
- Use machine learning to build a fast, accurate emulator of radio emission from extensive air showers, replacing slower simulations for GRAND analysis.
- Establish a GRAND team at San Francisco State where undergraduate and master's students learn numerical modeling and research computing.
More about the research
To study the most extreme events, scientists can instead look for ultra-high-energy (UHE) neutrinos, nearly massless particles that pass through matter almost untouched. These neutrinos could help answer questions about dark matter, new particles beyond the reach of the Large Hadron Collider, and new fundamental symmetries.
Why it matters
This is basic science. Detecting ultra-high-energy neutrinos could reveal where the universe's most energetic particles come from and test ideas about dark matter and new physics beyond particle colliders. The machine learning tools for signal cleanup and shower reconstruction could support GRAND's contributions to multi-messenger astronomy.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Kaeli Hughes
Ohio State University, OH·Physics
Develop high-fidelity simulations of the radio emission produced when neutrinos and cosmic rays interact in and above Greenland's glacial ice, for use in analyzing RNO-G data.
Ke Fang
University of Wisconsin-Madison, WI·Astronomy & Astrophysics·Early-career
Search for PeVatrons, sources that accelerate protons above 1 PeV, by jointly analyzing high-energy neutrino data from IceCube and gamma-ray data from the HAWC observatory.
Ignacio Taboada
Georgia Institute of Technology, GA·Astronomy & Astrophysics
Develop calibrations and reconstruction algorithms for the new IceCube Upgrade sensors to improve low-energy sensitivity and reduce systematic errors across IceCube's existing neutrino dataset.
The school
San Francisco State University: 3 labs on PI Pages, $42M a year on research, 17 research doctorates a year.
Reach out
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