Indirect search for dark matter with micrOMEGAs2.4 (1004.1092v2)

Abstract: We present a new module of micrOMEGAs devoted to the computation of indirect signals from dark matter annihilation in any new model with a stable weakly interacting particle. The code provides the mass spectrum, cross-sections, relic density and exotic fluxes of gamma rays, positrons and antiprotons. The propagation of charged particles in the Galactic halo is handled with a new module that allows to easily modify the propagation parameters.

• The paper introduces advanced micrOMEGAs2.4 features that compute comprehensive dark matter annihilation signals including gamma-ray lines.
• It details methods to model charged particle propagation in the galactic halo using modifiable astrophysical parameters.
• The work integrates higher-order effects and halo clumping to enhance predictions for indirect detection in diverse DM models.

Indirect Search for Dark Matter with micrOMEGAs_2.4

The development of software tools that facilitate the paper of dark matter (DM) properties interacting with the galactic environment is crucial for the interpretation of current astronomical observations and experiments. The paper presents an essential enhancement to the micrOMEGAs software, introducing advanced functionalities for computing indirect detection signals of dark matter, specifically focusing on a stable weakly interacting particle framework. The additions enable users to derive mass spectra, cross-sections, relic densities, and exotic fluxes of gamma rays, positrons, and antiprotons resulting from dark matter annihilation in theoretical models.

Core Enhancements

The paper highlights several key features incorporated into the new version 2.4 of micrOMEGAs, which include:

1. Extended Annihilation Processes: The software computes annihilation cross-sections for all two-body tree-level processes in any model, incorporating processes that yield gamma-ray lines, such as $\gamma\gamma$ and $\gamma Z^0$ in the Minimal Supersymmetric Standard Model (MSSM).
2. Propagation Mechanisms: Charged particles' propagation through the Galactic halo is detailed, allowing modifications of propagation parameters to explore different astrophysical scenarios. This is vital for correctly modeling energy loss and spatial diffusion, contributing significantly to detecting indirect signals of dark matter.
3. Inclusion of Higher-Order Effects: The ability to include radiative emissions and polarized gauge bosons in the spectrum calculations is crucial for more accurate results. Particularly, the representation of annihilation cross-sections with radiative photon emission can provide insights into models with parity constraints.
4. Halo Profiles and DM Clumping: The modeling framework for DM identifies the consideration of DM clumps' effect on flux calculation, acknowledging the sensitivity of results to the structure of dark matter distributions within the halo models.

Practical and Theoretical Implications

The software plays a significant role in advancing DM research by enabling theoretical predictions of indirect detection signals. It supports a broader range of particle physics models, including supersymmetric extensions, models with extra dimensions, and less conventional configurations like the little Higgs model. By incorporating a flexible toolkit for halo models and particle propagation, micrOMEGAs v2.4 allows researchers to simulate and analyze observational data vis-à-vis theoretical scenarios. These predictions are integral to refining searches for potential signals in the data obtained from instruments like Fermi, HESS, and PAMELA.

Future Directions in AI and Astrophysics

The paper suggests enhancements in micrOMEGAs could substantially affect future astrophysical studies and AI’s role in complex multiparametric analyses like the anisotropy of cosmic rays or gamma-ray emissions inferred from DM interactions. Advanced algorithms within AI frameworks could optimize these calculations, manage vast datasets, and facilitate real-time model evaluations against observational data. Speculating on long-term developments, AI technology may expand to integrate dynamic modeling environments, allowing self-adjusting simulations that respond to new empirical data, thus revolutionizing DM search methodologies.

In conclusion, the paper illustrates the potential transformational impact of introducing sophisticated computational tools like micrOMEGAs_2.4 in indirect DM detection research. These developments demonstrate a step forward in understanding phenomena such as weakly interacting massive particle (WIMP) annihilations, while emphasizing the reliance on modeling assumptions and propagation parameters, and it provides a groundwork for future enhancements at the confluence of computational astrophysics and theoretical particle physics.