An independent search for annual modulation and its significance in ANAIS-112 data

TL;DR

This study independently tests for annual modulation signals in ANAIS-112 dark matter data using multiple statistical methods, finding no significant evidence for modulation and extending previous analyses with novel Bayesian techniques.

Contribution

First application of Bayesian and information theory methods to analyze ANAIS-112 data for annual modulation signals, providing a new statistical perspective.

Findings

Bayesian analysis decisively favors no modulation hypothesis in non-background subtracted data.

Other model comparison techniques do not decisively favor any hypothesis.

Analysis codes are publicly available for transparency and reproducibility.

Abstract

We perform an independent search for sinusoidal-based modulation in the recently released ANAIS-112 data, which could be induced by dark matter scatterings. We then evaluate this hypothesis against the null hypothesis that the data contains only background, using four different model comparison techniques. These include frequentist, Bayesian, and two information theory-based criteria (AIC and BIC). This analysis was done on both the residual data (by subtracting the exponential fit obtained from the ANAIS-112 Collaboration) as well as the total (non-background subtracted) data. We find that according to the Bayesian model comparison test, the null hypothesis of no modulation is decisively favored over a cosine-based annual modulation for the non-background subtracted dataset in 2-6 keV energy range. None of the other model comparison tests decisively favor any one hypothesis over another. This is the first application of Bayesian and information theory techniques to test the annual modulation hypothesis in ANAIS-112 data, extending our previous work on the DAMA/LIBRA and COSINE-100 data. Our analysis codes have also been made publicly available.