On the sensitivity of present direct detection experiments to WIMP-quark and WIMP-gluon effective interactions: a systematic assessment and new model-independent approaches

TL;DR

This paper systematically assesses how current dark matter direct detection experiments constrain WIMP interactions with quarks and gluons, introducing a model-independent method to interpret these bounds across various couplings.

Contribution

It presents a new approximate, model-independent approach to derive bounds on WIMP-quark and WIMP-gluon interactions from experimental data, simplifying complex relativistic effective theory analyses.

Findings

The proposed method closely matches full calculations in most cases.

It enables quick evaluation of bounds for arbitrary coupling combinations.

A Python interface facilitates practical application of the method.

Abstract

Assuming for Weakly Interacting Massive Particles (WIMPs) a Maxwellian velocity distribution in the Galaxy we provide an assessment of the sensitivity of existing Dark Matter (DM) direct detection (DD) experiments to operators up to dimension 7 of the relativistic effective field theory describing dark matter interactions with quarks and gluons . In particular we focus on a systematic approach, including an extensive set of experiments and large number of couplings, both exceeding for completeness similar analyses in the literature. The relativistic effective theory requires to fix one coupling for each quark flavor, so in principle for each different combination the bounds should be recalculated starting from direct detection experimental data. To address this problem we propose an approximate model-independent procedure that allows to directly calculate the bounds for any combination of couplings in terms of model-independent limits on the Wilson coefficients of the non-relativistic theory expressed in terms of the WIMP mass and of the neutron-to-proton coupling ratio $c^n/c^p$. We test the result of the approximate procedure against that of a full calculation, and discuss its possible pitfalls and limitations. We also provide a simple interpolating interface in Python that allows to apply our method quantitatively.