Bayesian Analysis of time dependence of DAMA annual modulation amplitude

TL;DR

This paper applies Bayesian model comparison to analyze the time dependence of DAMA/LIBRA's annual modulation amplitudes, finding some evidence for variability in earlier data but no significant variability in recent datasets.

Contribution

It introduces Bayesian techniques to test for time dependence in DAMA data and compares results across different phases, providing a new statistical perspective.

Findings

Moderate Bayesian evidence for exponential variation in earlier DAMA data.

No significant variability found in recent DAMA/LIBRA datasets.

Analysis codes and datasets are publicly available.

Abstract

We implement a test of the variability of the per-cycle annual modulation amplitude in the different phases of the DAMA/LIBRA experiment using Bayesian model comparison. Using frequentist methods, a previous study (Kelso et al 2018) had demonstrated that the DAMA amplitudes spanning over the DAMA/NaI and the first phase of the DAMA/LIBRA phases, show a mild preference for time-dependence in multiple energy bins. With that motivation, we first show using Bayesian techniques that the aforementioned data analyzed in Kelso et al, show a moderate preference for exponentially varying amplitudes in the 2-5 and 2-6 keV energy intervals. We then carry out a similar analysis on the latest modulation amplitudes released by the DAMA collaboration from the first two phases of the upgraded DAMA/LIBRA experiment. We also analyze the single-hit residual rates released by the DAMA collaboration to further look for any possible time-dependency. However, we do not find any evidence for variability of either of the two datasets by using Bayesian model selection. All our analysis codes and datasets have been made publicly available.