Multi-Dimensional Effective Field Theory Analysis for Direct Detection of Dark Matter
H. E. Rogers, D. G. Cerdeno, P. Cushman, F. Livet, V., Mandic

TL;DR
This paper introduces a multidimensional effective field theory framework combined with Bayesian inference to analyze direct detection data of dark matter, enabling more comprehensive parameter constraints and model discrimination.
Contribution
It develops a systematic analysis method for dark matter direct detection using EFT and Bayesian inference, expanding parameter space and improving model selection capabilities.
Findings
Enhanced limits on WIMP parameters through joint likelihood analysis.
Identification of explicit momentum dependence in dark matter scattering.
Demonstrated the effectiveness of the method with simulations of nonstandard operators.
Abstract
The scattering of dark matter particles off nuclei in direct detection experiments can be described in terms of a multidimensional effective field theory (EFT). A new systematic analysis technique is developed using the EFT approach and Bayesian inference methods to exploit, when possible, the energy-dependent information of the detected events, experimental efficiencies, and backgrounds. Highly dimensional likelihoods are calculated over the mass of the weakly interacting massive particle (WIMP) and multiple EFT coupling coefficients, which can then be used to set limits on these parameters and choose models (EFT operators) that best fit the direct detection data. Expanding the parameter space beyond the standard spin-independent isoscalar cross section and WIMP mass reduces tensions between previously published experiments. Combining these experiments to form a single joint likelihood…
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