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arXiv · 1705.07920

DarkBit: A GAMBIT module for computing dark matter observables and likelihoods

Torsten Bringmann·Jan Conrad·Jonathan M. Cornell·Lars A. Dal·Joakim Edsjö·Ben Farmer·Felix Kahlhoefer·Anders Kvellestad·Antje Putze·Christopher Savage·Pat Scott·Christoph Weniger·Martin White·Sebastian Wild
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Abstract

We introduce DarkBit, an advanced software code for computing dark matter constraints on various extensions to the Standard Model of particle physics, comprising both new native code and interfaces to external packages. This release includes a dedicated signal yield calculator for gamma-ray observations, which significantly extends current tools by implementing a cascade decay Monte Carlo, as well as a dedicated likelihood calculator for current and future experiments (gamlike). This provides a general solution for studying complex particle physics models that predict dark matter annihilation to a multitude of final states. We also supply a direct detection package that models a large range of direct detection experiments (DDcalc), and provides the corresponding likelihoods for arbitrary combinations of spin-independent and spin-dependent scattering processes. Finally, we provide custom relic density routines along with interfaces to DarkSUSY, micrOMEGAs, and the neutrino telescope likelihood package nuLike. DarkBit is written in the framework of the Global And Modular Beyond the Standard Model Inference Tool (GAMBIT), providing seamless integration into a comprehensive statistical fitting framework that allows users to explore new models with both particle and astrophysics constraints, and a consistent treatment of systematic uncertainties. In this paper we describe its main functionality, provide a guide to getting started quickly, and show illustrative examples for results obtained with DarkBit (both as a standalone tool and as a GAMBIT module). This includes a quantitative comparison between two of the main dark matter codes (DarkSUSY and micrOMEGAs), and application of DarkBit's advanced direct and indirect detection routines to a simple effective dark matter model.

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Torsten Bringmann, Jan Conrad, Jonathan M. Cornell, Lars A. Dal, Joakim Edsjö, Ben Farmer, Felix Kahlhoefer, Anders Kvellestad, Antje Putze, Christopher Savage, Pat Scott, Christoph Weniger, Martin White, Sebastian Wild. 2018-01-09. DarkBit: A GAMBIT module for computing dark matter observables and likelihoods. https://doi.org/10.1140/epjc%2Fs10052-017-5155-4

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