PhyLiNO: A Forward-Folding Likelihood-Fit Framework for Neutrino Oscillation Physics

TL;DR

PhyLiNO is a high-performance, flexible likelihood-fit framework for neutrino oscillation data analysis, capable of handling multiple datasets and complex models efficiently with GPU acceleration.

Contribution

It introduces a novel forward-folding likelihood framework that efficiently analyzes multi-experiment neutrino oscillation data with rapid convergence and GPU optimization.

Findings

Successfully applied to Double Chooz data for multi-flavor oscillation fits.

Demonstrated applicability to JUNO-like medium baseline reactor experiments.

Achieved convergence times of a few seconds for hundreds of fit parameters.

Abstract

We present a framework for the analysis of data from neutrino oscillation experiments. The framework performs a profile likelihood fit and employs a forward-folding technique to optimize its model with respect to the oscillation parameters. It is capable of simultaneously handling multiple datasets from the same or different experiments and their correlations. The code of the framework is optimized for performance and allows for convergence times of a few seconds handling hundreds of fit parameters, thanks to multi-threading and usage of GPUs. The framework was developed in the context of the Double Chooz experiment, where it was successfully used to fit three- and four-flavor models to the data, as well as in the measurement of the energy spectrum of reactor neutrinos. We demonstrate its applicability to other experiments by applying it to a study of the oscillation analysis of a medium baseline reactor experiment similar to JUNO.