Abstract
Uncertainty in the local dark matter velocity distribution is a key difficulty in the analysis of data from direct detection experiments. Here we propose a new approach for dealing with this uncertainty, which does not involve any assumptions about the structure of the dark matter halo. Given a dark matter model, our method yields the velocity distribution which best describes a set of direct detection data as a finite sum of streams with optimised speeds and densities. The method is conceptually simple and numerically very efficient. We give an explicit example in which the method is applied to determining the ratio of proton to neutron couplings of dark matter from a hypothetical set of future data.
Highlights
The direct search for dark matter (DM) in shielded underground detectors is a promising strategy for confirming the particle nature of DM, and for measuring its key properties, such as mass and couplings to nucleons
Such uncertainties are important for light DM, which only probes the tail of f (v) [7, 8], but they significantly reduce the amount of information that can be inferred about general DM candidates
In this letter we propose a new method for dealing with uncertainties in the DM velocity distribution
Summary
The direct search for dark matter (DM) in shielded underground detectors is a promising strategy for confirming the particle nature of DM, and for measuring its key properties, such as mass and couplings to nucleons. Numerical simulations indicate that assuming an isotropic and isothermal halo may not be a good approximation [4] and that in addition there may be localised streams of DM [5] as well as a DM disk co-rotating with the stars [6] Such uncertainties are important for light DM, which only probes the tail of f (v) [7, 8], but they significantly reduce the amount of information that can be inferred about general DM candidates. In analogy to the treatment of the DM speed distribution f (v) = f (v)dΩv in [14, 15], we will write the velocity integral as a sum of step functions This approach allows us to have a very large number of free parameters and removes the need to make any assumptions about the form of f (v). Our method involves a frequentist rather than a Bayesian approach, so that no prior distributions for any of the parameters need to be proposed
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