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We present DARKFLUX, a software tool designed to analyze indirect-detection signatures for next-generation models of dark matter (DM) with multiple annihilation channels. Version 1.0 of this tool accepts user-generated models with 2 -> 2 tree-level dark matter annihilation to pairs of Standard Model (SM) particles and analyzes DM annihilation to gamma rays. The tool consists of three modules, which can be run in a loop in order to scan over DM mass if desired: (I) The annihilation fraction module calls an internal installation of MADDM, a dark matter phenomenology plugin for the Monte Carlo event generator MADGRAPH5_AMC@NLO, to compute the thermally averaged cross section (i) for each annihilation channel chi chi((chi) over bar, chi(dagger)) -> i is an element of {SM, SM}. The module then computes the fractional annihilation rate (annihilation fraction) into each channel. (II) The flux module combines the flux spectrum from each annihilation channel, weighted by the appropriate annihilation fractions, to compute the total flux at Earth due to DM annihilation. In DARKFLUX v1.0, this module specifically computes the gamma-ray flux for each channel using the publicly available PPPC4DMID tables. (III) The analysis module compares the total flux to observational data and computes the upper limit at 95% confidence level (CL) on the total thermally averaged DM annihilation cross section. In DARKFLUX v1.0, this module compares the total gamma-ray flux to a joint-likelihood analysis of fifteen dwarf spheroidal galaxies (dSphs) analyzed by the Fermi-LAT collaboration. DARKFLUX v1.0 automatically provides data tables and can plot the output of these three modules. In this manual, we briefly motivate this indirect-detection computer tool and review the essential DM physics. We then describe the several modules of DARKFLUX in greater detail. Finally, we show how to install and run DARKFLUX and provide two worked examples demonstrating its capabilities. DARKFLUX is available on GitHub at https://github.com/carpenterphysics/DarkFlux. (C) 2022 Elsevier B.V. All rights reserved.
Marcos Rubinstein, Antonio Sunjerga, Amirhossein Mostajabi
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