Doojin Kim
University of South Dakota, SD·Physics
Most of the matter in the universe appears to be dark matter, which has never been detected except through gravity. Neutrino experiments, which fire intense particle beams into huge detectors, offer a new place to look for light, weakly interacting particles.
- Fields
Physics
Astronomy & Astrophysics
Computer Science
- Topics
particle physics
high-energy physics
neutrino physics
- Grants
- 2 known grants, through Dec 2028
What they work on
- Develop theory frameworks for MeV-scale dark-sector physics at beam neutrino experiments, including light Kaluza-Klein gravitons from extra dimensions and rare three-body charged-meson decays that could produce light mediators.
- Build machine learning classifiers that distinguish sterile-neutrino, non-standard-interaction and low-mass dark matter signals using timing, energy, angle and position information in experiments such as DUNE, SBND and ICARUS.
- Create fast empirical models of charged-meson focusing by magnetic horns to reduce reliance on computationally expensive horn simulations in neutrino beam studies.
- Extend qubit-based photon detection, which uses quantum bits as ultra-sensitive light sensors, to search for narrow photon signals from axion-like particles and relic-neutrino-related signals.
- Construct machine-learning detector-response models for DUNE's liquid-argon time-projection chambers that predict efficiencies, particle identification and reconstructed quantities directly from true particle information.
- Release a validated, documented, standalone fast-simulation package that produces analysis-level results without full-detector simulation, developed with Fermilab collaborators.
More about the research
Their vast datasets, however, are costly to simulate and analyze. These two projects combine particle theory, machine learning and quantum sensing to search for such particles and to speed up the computer tools that neutrino physics relies on.
Why it matters
This is fundamental physics: the work could help reveal what dark matter is and whether particles beyond the Standard Model exist. Its practical output is reusable open software, including fast detector simulations and machine-learning classifiers. These tools could cut the computing cost of designing and analyzing DUNE and future liquid-argon neutrino experiments, and could guide quantum-sensor searches for very faint signals.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Mark Ross-Lonergan
Columbia University, NY·Physics
Analyze the full MicroBooNE dataset, combining several reconstruction methods and about twenty final-state patterns, to test whether a single-photon excess explains the MiniBooNE anomaly through new physics or the Standard Model.
John Parsons
Columbia University, NY·Physics
Measure properties of the Higgs boson with the ATLAS detector at the Large Hadron Collider to probe fundamental particles and possible links to dark energy.
Suyog Shrestha
Washington College, MD·Physics
Search ATLAS data from the Large Hadron Collider for new Higgs-like scalar particles that decay into two bottom quarks and two W bosons, in final states with one or two leptons, a decay channel ATLAS has not yet studied.
The school
University of South Dakota: 4 labs on PI Pages, $28M a year on research, 52 research doctorates a year.
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