Using End-to-End Optimized Summary Statistics to Improve IceCube's Diffuse Galactic Fits

TL;DR

This paper introduces an end-to-end neural network approach to optimize summary statistics for IceCube neutrino data analysis, enhancing the resolution of galactic neutrino flux models by effectively utilizing more observables despite limited Monte Carlo statistics.

Contribution

It presents a novel neural network-based method to optimize summary statistics for neutrino flux analysis, improving model resolution and handling higher-dimensional data.

Findings

Enhanced likelihood contour resolution for galactic neutrino flux models

Increased effective use of observables in data analysis

Demonstrated improved analysis performance with limited MC statistics

Abstract

Characterizing the astrophysical neutrino flux with the IceCube Neutrino Observatory traditionally relies on a binned forward-folding likelihood approach. Insufficient Monte Carlo (MC) statistics in each bin limits the granularity and dimensionality of the binning scheme. A neural network can be employed to optimize a summary statistic that serves as the input for data analysis, yielding the best possible outcomes. This end-to-end optimized summary statistic allows for the inclusion of more observables while maintaining adequate MC statistics per bin. This work will detail the application of end-to-end optimized summary statistics in analyzing and characterizing the galactic neutrino flux, achieving improved resolution in the likelihood contours for selected signal parameters and models.