# Robust model comparison tests of DAMA/LIBRA annual modulation

## TL;DR

This paper rigorously evaluates the DAMA/LIBRA annual modulation claims using multiple statistical methods, including frequentist, information theory, and Bayesian approaches, and introduces the first application of AIC, BIC, and Bayes factor to this data.

## Contribution

It presents a comprehensive, multi-method statistical analysis of DAMA/LIBRA data, applying novel model comparison techniques to assess the significance of the claimed annual modulation.

## Key findings

- No significant evidence for annual modulation in DAMA/LIBRA data
- Demonstrates the effectiveness of AIC, BIC, and Bayes factor in analyzing dark matter detection data
- Provides publicly available analysis code and data for reproducibility

## Abstract

We evaluate the statistical significance of the DAMA/LIBRA claims for annual modulation using three independent model comparison techniques, viz frequentist, information theory, and Bayesian analysis. We fit the data from the DAMA/LIBRA experiment to both cosine and a constant model, and carry out model comparison by choosing the constant model as the null hypothesis. For the frequentist test, we invoke Wilk's theorem and calculate the significance using $\Delta \chi^2$ between the two models. For information theoretical tests, we calculate the difference in Akaike Information Criterion (AIC) and Bayesian Information criterion (BIC) between the two models. We also compare the two models in a Bayesian context by calculating the Bayes factor. We also search for higher harmonics in the DAMA/LIBRA data using generalized Lomb-Scargle periodogram. We finally test the sensitivity of these model comparison techniques in discriminating between pure noise and a cosine signal using synthetic data. This is the first proof of principles application of AIC, BIC as well as Bayes factor to the DAMA data. All our analysis codes along with the data used in this work have been made publicly available.