Search for periodicities in experimental data using an autoregression data model

TL;DR

This paper explores spectral analysis methods based on autoregression models, like maximum entropy, Pisarenko, and Prony's methods, to identify periodicities in high-energy physics experimental data, demonstrating their effectiveness with real and simulated data.

Contribution

It introduces and evaluates autoregression-based spectral analysis techniques for detecting periodicities in high-energy physics data, highlighting their potential advantages.

Findings

Autoregression methods effectively identify periodicities in experimental data.

Maximum entropy, Pisarenko, and Prony's methods provide consistent results.

The methods work well with both experimental and simulated data.

Abstract

To process data obtained during interference experiments in high-energy physics, methods of spectral analysis are employed. Methods of spectral analysis, in which an autoregression model of experimental data is used, such as the maximum entropy technique as well as Pisarenko and Prony's method, are described. To show the potentials of the methods, experimental and simulated hummed data are discussed as an example.