Robust model comparison tests of DAMA/LIBRA annual modulation
  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.
