Neutrino unfolding with machine learning
2026 — present
With Prof. Benjamin Nachman, I’m testing how well machine learning can correct for detector effects in MINERvA neutrino data.
Details and links
I am implementing and evaluating OmniFold on MINERvA neutrino-scattering data with Prof. Benjamin Nachman, comparing it with iterative Bayesian unfolding through closure tests, generator stress tests, and bootstrap resampling.
Work in progress: whether high-dimensional unbinned unfolding reduces structural model bias is the question under test.
What is unfolding?
Estimate the physics before the detector changes what we see.
1. Underlying physics
The distribution we want to measure.
2. Detector measurement
Detector effects spread and distort it.
3. Unfolded estimate
Simulation helps estimate the original shape.
OmniFold changes how much each simulated event counts, using its paired descriptions before and after detection. Those weights are learned by comparing simulation with measured data.
The starting simulation and detector model matter. The result is an estimate with uncertainties; individual measured events do not get their original values back.
Try the interactive version
Watch OmniFold adjust the simulation to match measured data.
The interactive example loads when opened.


