A New Method for Analysing Dark Matter Direct Detection Data

International audienceThe experimental situation of Dark Matter Direct Detection has reached anexciting cross-roads, with potential hints of a discovery of Dark Matter (DM)from the CDMS, CoGeNT, CRESST-II and DAMA experiments in tension withnull-results from xenon-based experiments such as XENON100 and LUX. Given thepresent controversial experimental status, it is important that the analyticalmethod used to search for DM in Direct Detection experiments is both robust andflexible enough to deal with data for which the distinction between signal andbackground points is difficult, and hence where the choice between setting alimit or defining a discovery region is debatable. In this article we propose anovel (Bayesian) analytical method, which can be applied to all DirectDetection experiments and which extracts the maximum amount of information fromthe data. We apply our method to the XENON100 experiment data as a workedexample, and show that firstly our exclusion limit at 90% confidence is inagreement with their own for the 225 Live Days data, but is several timesstronger for the 100 Live Days data. Secondly we find that, due to the twopoints at low values of S1 and S2 in the 225 days data-set, our analysis pointsto either weak consistency with low-mass Dark Matter or the possible presenceof an unknown background. Given the null-result from LUX, the latter scenarioseems the more plausible