Detection of Small Scale Components in Power Law Spectra

TL;DR

This paper introduces a novel functional data analysis method to detect small components in steep power-law spectra, exemplified by muon and neutrino energy spectra, overcoming challenges posed by large systematic uncertainties.

Contribution

It presents a new approach based on functional data analysis for identifying small spectral components in power-law spectra, improving detection despite systematic uncertainties.

Findings

Method successfully detects small spectral components.

Application to muon and neutrino spectra demonstrates effectiveness.

Provides insights into astrophysical and conventional components.

Abstract

Spectra in astroparticle physics are commonly approximated by simple power laws. The steeply falling nature of these power laws, however, makes the detection of additional components rather challenging. This holds true especially, if the additional components are small compared to the established ones. Energy spectra of muon neutrinos are an interesting example of such a scenario, where the conventional and astrophysical components to the spectra have been established by the use of different analysis methods, such as likelihood fits or spectral deconvolution. The prompt component, although expected from theoretical models, has not yet been experimentally observed. Furthermore, the extraction of physics parameters is challenged by the large systematic uncertainties, especially at high energies. This contribution presents a different approach to the analysis of power-law spectra, which is based on functional data analysis. The method itself and its implications are discussed using muon and neutrino energy spectra as an example.